Visual attention is thought to be driven by the interplay between low-level visual features and task dependent information content of local image regions, as well as by spatial viewing biases. Though dependent on experimental paradigms and model assumptions, this idea has given rise to varying claims that either bottom-up or top-down mechanisms dominate visual attention. To contribute toward a resolution of this discussion, here we quantify the influence of these factors and their relative importance in a set of classification tasks. Our stimuli consist of individual image patches (bubbles). For each bubble we derive three measures: a measure of salience based on low-level stimulus features, a measure of salience based on the task dependent information content derived from our subjects' classification responses and a measure of salience based on spatial viewing biases. Furthermore, we measure the empirical salience of each bubble based on our subjects' measured eye gazes thus characterizing the overt visual attention each bubble receives. A multivariate linear model relates the three salience measures to overt visual attention. It reveals that all three salience measures contribute significantly. The effect of spatial viewing biases is highest and rather constant in different tasks. The contribution of task dependent information is a close runner-up. Specifically, in a standardized task of judging facial expressions it scores highly. The contribution of low-level features is, on average, somewhat lower. However, in a prototypical search task, without an available template, it makes a strong contribution on par with the two other measures. Finally, the contributions of the three factors are only slightly redundant, and the semi-partial correlation coefficients are only slightly lower than the coefficients for full correlations. These data provide evidence that all three measures make significant and independent contributions and that none can be neglected in a model of human overt visual attention. In our lifetime we make about 5 billion eye movements. Yet our knowledge about what determines where we look at is surprisingly sketchy. Some traditional approaches assume that gaze is guided by simple image properties like local contrast (low-level features). Recent arguments emphasize the influence of tasks (high-level features) and motor constraints . The relative importance of these factors is still a topic of debate. In this study, subjects view and classify natural scenery and faces while their eye movements are recorded. The stimuli are composed of small image patches. For each of these patches we derive a measure for low-level features and spatial bias. Utilizing the subjects' classification responses, we additionally derive a measure reflecting the information content of a patch with respect to the classification task (high-level features). We show that the effect of spatial bias is highest, that high-level features are a close runner-up, and that low-level features have, on average, a smaller influence. Remarkably, the different contributions are mostly independent. Hence, all three measures contribute to the guidance of eye movements and have to be considered in a model of human visual attention. In daily life, eye movements center parts of a scene on the human fovea several times a second Attention models designed to cope with the complexities of natural conditions are usually based on a so-called salience map A major concern is that even if features of the salience map, such as luminance contrast, are good correlates of fixation probability, they do not necessarily drive attention causally For these reasons, there is consensus that viable models of human attention should not rely solely on stimulus properties. Specifically, eye movements are influenced by spatial constraints and properties of the oculomotor system. A wide range of studies has demonstrated a preponderance of small amplitude saccades That the task context influences eye movements has long been observed http://labelme.csail.mit.edu/). An explicit quantification of the contribution of task dependent factors relative to feature-based factors is still missing.It was suggested early on that in a specific task context, the information content of an image patch defines its salience In summary, it is widely acknowledged that image features, the task of the observer, and the properties of the oculomotor system contribute to the selection of fixation points. Still, in the absence of quantitative data on the relative weight of the different factors, settling the issue of how exactly each of these contributes towards overt attention is not possible. In the present study, we attempt a step in this direction: we quantify the relative contribution of stimulus properties, task dependence, and oculomotor constraints to the selection of fixation points. We capture stimulus dependent properties by an analysis of low-level image features. Task dependent factors are captured by the information content of discrete parts of the stimulus in well defined tasks. The influence of oculomotor constraints is taken into account by a generative model including typical saccadic parameters and the central bias. With this approach we obtain scalars for each of these three factors for each image region, allowing us to quantify their independent contributions to human eye movements.To quantify the three different types of influences we sample non-overlapping image patches (bubbles) from forest scenes and face images. These isolated patches are shown in different configurations in combination with a classification task. This design is inspired by Gosselin and Schyns, who have introduced the bubble paradigm to measure which parts of an image are used by the observer to solve a classification task In comparison to full field stimuli, our bubble stimuli consist of a manageable number of discrete perceptual units. Using discrete units allows us to assign a single value for each of the measures to each bubble. In particular, describing the task dependent information of a bubble using the degree of agreement between subjects with respect to a classification task requires individual pieces of visual information. It is not clear how a measurement of local information content could be achieved using full field stimuli only. Accordingly, the problem of measuring local information is exactly the one addressed when the bubbles technique was first established Having acquired the four measures for each bubble, we finally use linear multivariate regression to quantify the overall and the individual, i.e., non-redundant, contributions of the task- dependent, feature-based, and spatial-based factors influencing attention.gender or according to facial expression, with the stimulus classeshappy, sad, fearful, or disgusted. For the stimuli based on forest scenes, one task (space) was to decide whether the scenery was close and narrow – when the image was a close-up or displayed a closed environment – or whether it was wide and open. The other task was to judge the presence of indicators of human influence such as houses, roads, paths, trunks of trees. Stimulus presentations lasted for three seconds during which the subjects' eye movements were recorded. The majority of the stimuli were composed of 1 to 5 bubbles placed on a gray background. Half of the stimuli consisted of bubbles originating from the same full field image (condition same), whereas 15% of the stimuli combined bubbles from different full field images belonging to the same stimulus class (condition congruent). Another 15% of the stimuli were composed of bubbles originating from full field images of different classes (condition incongruent). To control for position effects, we also showed stimuli (16%) in which the positions of the bubbles are shuffled (condition permuted). The remaining 4% of the stimuli were full field images, which we used to confirm that subjects agreed on the classes of the images underlying the bubble stimuli. The bubbles themselves were constructed from square image patches with a side length of 6 visual degrees. To each patch, we applied a space-variant filter to imitate the retinal resolution when fixating the center of the bubble and a Gaussian mask to avoid visible edges.In this study, subjects had to classify visual stimuli based either on face images or on forest scenes. We employed a total of four different tasks. Face stimuli had to be classified either according to 75 subjects took part in this study, each performed 280 trials. We used a total of 2061 gray-scale stimuli for all subjects. This resulted in 21000 trials, recorded with 131935 fixations.In a first step, we investigated viewing behavior relative to bubbles. Subjects made, on average, 6.2 fixations in each trial where bubbles were presented. Of these, 94% were no more than 3 visual degrees distant from the closest bubble center and thus were located well inside a bubble. Three percent were located at the screen center and can be attributed to anticipation of the decision screen that followed each trial. The remaining 3% were scattered across the screen. Hence, the fixations were rare in the space between bubbles and were clearly targeted at bubbles.We designed the bubbles in such a way that maximal and complete information is available when subjects fixated the center of the bubble see . Hence sBuilding on the property that bubbles are treated as units, we derive a measure characterizing the empirical salience of a complete bubble. It is based on the fixation counts of a bubble in specific stimulus configurations (see below). In the above example of expression, gender, influence, and space). When comparing the different tasks, please note that in expression the chance level is 25% and in all others 50%. The high-level of performance indicates that participants understood the tasks and had a shared interpretation of stimulus classes.The average classification performance for the original images (full field stimuli) was 94%, as measured by the fraction of responses that were correct with respect to the image class established in pre-experiments. For the four tasks average performance was 87%, 94%, 99% and 94%  yielded average stimulus information of 1.18 bit, 0.66 bit, 0.74 bit, and 0.54 bit for the four tasks expression, gender, influence, and space, respectively. Presenting stimuli composed of bubbles taken from different images of the same response class (condition congruent) average stimulus information changed to 1.31 bit, 0.62 bit, 0.67 bit, and 0.61 bit . In contrast, in presenting stimuli composed of bubbles taken from images of different response classes (condition incongruent) it dropped to 1.12 bit, 0.55 bit, 0.58 bit, and 0.34 bit. These data demonstrate that stimulus information is far from complete and that no ceiling effects are to be expected.Next we investigated stimulus information in the reduced stimuli composed of bubbles. Presenting bubble stimuli composed of bubbles taken from the same base image  compared to the prediction of the p-model (dashed black line). This higher-than-expected stimulus information indicates a violation of the assumption of independence of the information in different bubbles and is investigated below. In the gender task, which also uses face stimuli, at four and five bubbles a surplus of measured information is observed as well. Due to the larger variance of these two data points it does not reach significance. Stimulus information in the influence task, which uses natural scenes, is well predicted by the p-model, and no significant deviation of estimate and data could be detected . For space, stimulus information is a little, but significantly, smaller than predicted by the p-model . In this condition, the integration strategy of the subjects does not quite reach optimal performance. These data suggest that the p-model provides a reasonable description of the information integration. The mentioned deviations are further investigated below.To address the integration of information we analyze stimulus information as a function of the number of bubbles . First, tion see . The modsame and congruent. In contrast to the former, the latter is composed of bubbles that originate from different full field images of the same response class. Data obtained in same and congruent conditions give rise to nearly identical values of stimulus information in all tasks, and their difference is never significant  are given more weight, irrespective of the actual content of the bubble . Indeed, the main result for permuted stimuli is an improved fit by the p-model. On the other hand, position effects are not a likely cause for the deviations in the space task. There, the permuted and same conditions show no pronounced differences. The stimulus information for both is slightly below that of the p-model.To further elucidate the cause for the deviations of the data from the p-model estimates, we consider the interaction of bubble information and spatial location. For this purpose, we employ permuted stimuli. These are composed of bubbles placed at positions not matching their location in the respective full field stimuli see . In all gender, influence, and space, we performed an additional test and considered the differences between the response distributions of normal stimuli and their permuted versions. To specify whether these differences reflect a significant effect of permutation, we investigate whether the differences are consistent with the assumption that the responses for normal and permuted stimuli are sampled from the same stimulus answer distribution, independent of bubble arrangement. As the overwhelming majority (98.6%) of the differences between permuted and non permuted stimuli is located within the 95% confidence region of the zero hypothesis, no significant effect of permutation could be detected. It must be noted, however, that the test power is limited by the small number of trials using permuted stimuli.To test more directly whether bubble position and arrangement have an influence on information integration in the tasks same and the congruent condition, face stimuli consisting of many bubbles are processed using additional configural information We arrive at the conclusion that the p-model provides a good description of integration of information for face stimuli in the permuted condition and for forest scenes in all conditions. In the Now we address the relative contributions to fixation behavior of the stimulus dependent salience, task dependent information, and geometric properties of the stimuli. First, to address the stimulus dependent salience, we consider the low-level visual information of luminance contrast and texture contrast. These features are presumably processed in a bottom-up manner and have been used in other studies before. Second, to address the task dependent information, we consider the measure of bubble information, which captures the contribution of a bubble to the classification responses of subjects see . Third, In agreement with a large body of previous research The luminance and texture contrast of bubbles are determined by standard procedures see . To infeexpression task with the individual bubbles labeled with their stimulus dependent salience. Bubble A, located on the right eye and eyebrow, contains high luminance and texture contrasts. This is mapped to a high value of the stimulus dependent salience . Similarly, no significant correlation is observed for the space task . This implies that the deviation from the p-model stays within a factor of 2 of the theoretical lower limit and is consistent with the observation above that the p-model faithfully describes the dependence of stimulus information on the number of bubbles . Although there is a noticeable drop in correlation for the tasks gender, influence, and space; all are highly significant  as well  is highly significant  for all four tasks, but varies considerably across tasks. For expression, the multivariate correlation is highly significant, with 48% of variance explained. Bubble information is the best individual predictor, the pair-wise correlation being highly significant. The individual predictive power of spatial bias salience is smaller, but the pair-wise correlation is still highly significant. Stimulus dependent salience, on the other hand, does not significantly correlate with empirical salience. These results indicate that subjects have much information about where to expect informative bubbles a priori and that their attention is guided by this task dependent knowledge. This is exactly what one would expect of a system that is specialized in effectively recognizing facial expression. It is clearly inconsistent with a purely bottom-up driven account of overt attention. For the gender task, the multivariate correlation coefficient is smaller than for expression, but still highly significant, with 27% of variance explained. Spatial bias salience and bubble information have almost the same pair-wise correlation coefficient, both correlations being highly significant. In contrast to the expression task, the pair-wise correlation of the stimulus dependent salience is also significant. For the influence task, the multivariate correlation is also highly significant, with 36% of variance explained. Again all three predictors show significant, even highly significant, pair-wise correlations. Spatial bias salience has the highest correlation coefficient, followed by bubble information and stimulus dependent salience, the latter two being almost identical. For the space task, the multivariate correlation coefficient is smallest, but still highly significant, with 25% of variance explained. Spatial bias salience is the best predictor, followed by bubble information. Both these pair-wise correlations are highly significant. In contrast to influence, the correlation coefficient of stimulus dependent salience is very small and not significant.influence task, reflecting a rather small unique influence of bubble information on empirical salience, as well as a noticeable decrease of the semi-partial correlation coefficient compared to the pair-wise correlation coefficient for the spatial bias based predictor in the expression task.The previous observations on the relative predictive power of individual predictors in the different tasks are also supported by the semi-partial correlation analysis. The only exceptions are a rather large decrease from the pair-wise to the semi-partial correlation for bubble information in the influence, it is not even significant. Stimulus dependent salience is the weakest predictor of the three, but shows significant correlations in gender and influence. Each single predictor shows significant normal and semi-partial correlations in at least some of the tasks. Furthermore, the relative contributions of the predictors, in terms of both uncontrolled and semi-partial correlations, vary considerably over tasks. Hence the contribution of the three different factors is dependent on the task, and none can be generally dismissed in an explanation for guidance of overt attention.On the level of individual predictors, we make several observations. Spatial bias salience shows a strong and stable contribution in all four tasks. The unique contribution of bubble information is strong as well, but varies considerably over tasks. In the case of In this study, we quantify and compare the influence of low-level stimulus features, task dependent features, and spatial biases on overt visual attention. The major achievement is a direct and quantitative comparison of the individual influences of these factors on fixation behavior in a single study. The experimental approach builds on the bubble paradigm as introduced by Gosselin and Schyns gender and influence tasks. Bubble information is the best predictor in the expression task but for the other tasks it reaches slightly lower correlations with empirical salience than does spatial bias. Surprisingly, the semi-partial correlation coefficients, which reflect the unique contributions of each predictor controlling for the influences of the other factors, are only slightly lower than the pair-wise correlation coefficients. This indicates that all three factors act almost independently on visual attention. In summary, we find that all factors contribute, but that the absolute and relative strength of contribution depends on the task.Having measured the three factors bubble information, stimulus dependent salience, and spatial bias salience, we then quantified how well they predict fixation behavior. We found that a substantial portion of variance of empirical salience could be explained by all three factors combined, although the share of variance explained varies across tasks. Pair-wise correlations between empirical salience and each of the factors indicate clear differences between the three factors. Empirical salience shows high correlations with spatial bias throughout all four tasks, whereas both the correlations with stimulus dependent salience and bubble information vary strongly with tasks. Stimulus dependent salience is the weakest predictor, but reaches significant levels in the We now look into the potentially critical issues and shortcomings of our paradigm. These fall into two overall categories. First, we discuss the validity of our different measures. Second, we analyze how much the results obtained using our bubble paradigm generalize to more natural conditions.One basic assumption of the present approach is that the empirical saliences of different bubbles are independent from each other — i.e. the empirical salience of a bubble is not influenced by any other bubble on the same stimulus. An indicator of a violation of this assumption would be a change of the ratio of fixations falling onto two bubbles when other bubbles were presented simultaneously. We tested whether empirical salience values can predict the average number of fixations made by the subjects onto each bubble in all stimuli. The test resulted in very small errors showing that our assumption of independence between bubbles with respect to empirical salience is not violated.We characterized stimulus dependent salience as the conditional probability of fixating on an image patch given its local luminance and texture contrast for several reasons. First, these two low-level features were shown to correlate with fixation behavior in many previous studies th order) and geometric constraints on the length and direction of saccades (1st order). While the location of a particular fixation has an influence on the next fixation, we do not model higher order dependencies. Specifically, we do not account for inhibition of return, which would be a 2nd order relation of direction and length of saccades. Inhibition of return is characterized as a small delay of saccades that return to the location of a previous fixation. As the current investigation is not concerned with these dynamic aspects, it is not of relevance here. Furthermore, recent studies report that inhibition of return might actually not change viewing strategy for complex scenes We characterized spatial bias salience through a generative model of fixation behavior. The model takes into account the central bias of fixations , the latter would be the relevant measure as it matches the viewing conditions during the task. These reasons further grow our confidence in the validity of the applied methods.Several models of information integration are conceivable. The mode of information integration is an important topic in its own right and a complete treatment is beyond the scope of the present paper. We assume a probabilistic integration model but also considered two other models of information integration: first a local model that captures stimulus information by the maximally informative bubble, second a global model that differs from the probabilistic model by capturing contra factual evidence for the different choice possibilities. Compared to the p-model these models both show lower performance see . FurtherIn conclusion, although we did not show that a probabilistic model for information integration is the true or optimal model we demonstrated that the estimates for stimulus information obtained through it are robust and consistent with the majority of the data. The influence of configural information in face stimuli has been described before and does not pose a problem in the current context. The question of which is the optimal model of information integration is left to be answered by future research.For these reasons we decided to show stimuli with varying numbers of bubbles in a homogeneous set and to employ the information integration model and global fitting procedure. In so doing we assess the two behavioral measures, bubble information and empirical salience, from the same subjects during the same experimental trials and make optimal use of experimental data to improve the signal to noise level.same versus permuted in task expression . Since empirical salience of individual bubbles is well preserved on full field stimuli, we expect that our findings generalize to full scene viewing.Stimulus based salience, as we defined it, is just dependent on a local image patch. It is thus preserved for full field stimuli. Bubble information measures how much information with respect to a task is contained within a single bubble. The amount of information contained appears largely independent of bubble context and thus only depends on the image patch itself. Spatial bias salience, as we define it, is based on global fixation and saccade biases assessed from a large variety of full field stimuli. Hence, the effect of spatial bias should be largely independent of whether an image patch is embedded into a full field or bubble stimulus. The question of whether the measure of empirical salience is preserved on full field stimuli is more intricate. The observer may very well fixate image regions in the bubble stimuli that would never draw her attention given the complete image. We tested this by correlating empirical salience of bubbles with the fixation densities of the full field images containing those bubbles . Their ages ranged from 18 to 41, with a mean of 24.2 years. All had normal or corrected-to-normal vision, which was confirmed by a vision test with Landolt rings. Participation was voluntary, and participants either were granted extra course credits or received monetary compensation for their participation.2 is exceeded and a distance of at least 0.1° is covered with a minimal velocity of 30°/s. Fixation points are then defined by the samples in between two saccades. Stimuli were presented on a 21-inch Samsung Syncmaster 1100 DF 2004  CRT monitor at a distance of 80 cm from the subject, using a display resolution of 1024×786 pixels and a refresh rate of 120 Hz. These settings resulted in a spatial resolution of 33 pixels per degree of visual angle. No headrest was used.Participants' eye movements were recorded with the head-mounted Eyelink II eye-tracking system and the Eyelink II software package . Monocular eye-position data were sampled with infrared-based tracking only, using a sampling rate of 250 Hz. The saccade classification of the Eyelink system is based on velocity and acceleration. A saccade starts if an initial acceleration threshold of 8000°/stasks (see below) according to gender (gender) and expression (expression), forest images according to scenic openness (space) and human influence (influence). Only photographs that were evaluated consistently by all participants of the pre-experiments were included in the present study. These responses defined the different classes used below during stimulus construction. We selected a total of 24 photographs of faces and 36 photographs of forest scenes. The stimulus sets were balanced in the context of each of the four tasks. In 4% of all trials, stimuli were photographs shown in full field condition  were composed entirely of bubbles from the same full field image. Congruent stimuli (15%) were composed of bubbles from different full fields that were classified in the same way during the pre-experiments (they belong to the same class). Incongruent stimuli (15%) were composed of bubbles from full fields of different classes. Permutations (16%) were created by shuffling the positions of the bubbles. The final stimulus set was created using a randomized algorithm that optimized the set with respect to the constraint that each individual bubble should appear in the same number of stimuli.The final bubble stimuli were created by combining bubbles. In a small fraction, individual bubbles were shown (12%). The remaining stimuli were composed of two (42%), three (26%), four (14%) or five bubbles (2%). Combining several bubbles, depending on their full field stimulus of origin, allows different nditions . Same stexpression of the actors into the classes “happy,” “sad,” “fearful,” or “disgusted.” Similarly they classified gender into “male” or “female.” For the space task, participants were asked to choose between “close and narrow” or “wide and open.” They were instructed to respond “close and narrow” if the image was a close-up or if it would not be possible to leave the scene—for example, if leaves and branches were blocking the view. They were told to respond “open and wide” if it was possible to look far ahead. For the influence task, we asked participants to look for indicators of human influence such as houses, roads and paths, trunks of trees, fences, and hewn stones and to classify the stimuli into either “present” or “absent.” The wording of the instructions was the same for all participants.During the experiment, participants classified visual stimuli in four different tasks. In the first task, participants tagged stimuli according to the facial A complete experimental session lasted approximately one hour. It was divided into four blocks, one for each of the four classification tasks. Face stimuli and forest scene blocks were presented alternately. In the beginning of the experiment, participants were instructed about the procedure, and example bubble stimuli were shown. They were directed to classify the stimuli by pressing numbers on the keyboard's keypad and to take their best guess in cases where they were not sure about the stimulus' class.Before the beginning of each block, the eye tracker was calibrated, and task and answer choices for that block were explained and exemplified. Each block consisted of 70 trials that were presented in constrained random order (see below). Each trial began with the presentation of a fixation cross in the middle of the screen. Whenever the fixation of the cross indicated a notable decline in tracking quality, the eye tracker was recalibrated. This ensured that the mean tracking error for at least one eye was always lower than 0.4°. If the cross was fixated properly, the conductor of the experiment triggered the stimulus presentation. We excluded the very first fixation from all subsequent analysis, as it directly reflects the preceding fixation of the fixation cross. The trial lasted for 3 seconds and was followed by the answer screen, which stayed on until participants responded by using the keyboard. There was no time limit for the decision. Before the next trial started, visual feedback of the participant's response was given to minimize classification errors due to typos .The stimuli shown to each participant and their order were selected by a randomizing algorithm that respected the following constraints: for each participant, each stimulus was shown at most once; each bubble was presented at most four times; and stimuli with the same bubble were not shown in direct succession. Furthermore, on average, each stimulus should be shown to 8 participants, and the variation in the number of participants that have seen a particular stimulus should be as small as possible.In the following, we first define a measure for the empirical salience of bubbles as quantified by fixation probability. Then we derive measures for the spatial bias, and the stimulus dependent and task dependent effects. These three measures will be used to investigate the relative contributions to the empirical salience of stimuli. All three measures put the bubbles in a global order.S, the ratio between the number of fixations at a bubble A, S(A)F, and the number of fixations at another bubble B, S(B)F, is independent of the context in which both are presented. This impliesS with bubbles A and B, where AE and BE are global measures of empirical salience, which are independent of stimulus context. From this, it follows that the equationS and any bubble A. Because every stimulus was presented to several subjects, we have, in fact, several left-hand sides of this equation. We average them for each stimulus and bubble. Next, the resulting equations are grouped into a linear system, and we compute the empirical salience as the best approximate solution. We eliminate one degree of freedom by imposing a scale, demanding that all empirical saliences sum to one.To obtain a global quantification of empirical salience, we assume that on any stimulus To characterize the bottom-up contribution to fixation behavior, we use a feature-based salience model. It models the conditional probability of fixating a location of an image, given a set of local low-level image features. Here we consider luminance contrast and texture contrast as features.G, in close analogy to the computation of a bubble. Formally, the luminance contrast of a pixel, LC(x), is given byI(x) is the map of luminance intensity at each pixel, Δ is the displacement relative to the center of the bubble, and congruent and incongruent, several different full field stimuli contribute. There is no obvious generalization of an image-specific normalization procedure to these conditions. For these reasons we follow the suggestion of Zhang et al. taskI). This is based on the assumption that the influence of a bubble's contrast on the viewing behavior depends on the whole range of contrast values appearing in the images of one category. Luminance contrast is defined as the standard deviation of the luminance intensity in an image patch, normalized by the mean intensity of the entire image LC, used for the computation of the texture contrast, is calculated with a Gaussian window of a quarter of the size of a bubble. For the subsequent computation of texture contrasts, the same Gaussian window, G, as for bubble creation is used. The luminance contrast and texture contrast of a single bubble are defined as the contrast values at the center of the bubble.Texture contrast is defined as the standard deviation of the luminance contrast values in an image patch, normalized by the mean luminance contrast of the entire image Based on the feature contrasts of each bubble, we now derive a scalar describing the stimulus dependent contribution to fixation probability . In a prFor computational efficiency and optimal usage of data we bin the luminance contrast and texture contrast values of each image. We chose 20 bins with boundaries so that the number of available image locations falling into each bin is constant. Next, the probability of a feature value (luminance contrast or texture contrast) occurring at a fixated location was calculated. Then priors on the image features and fixation locations are computed. The priors on the image features are constant due to the equilibration of the distribution. The priors for the fixation locations were estimated for each image category. Both the feature and fixation location priors were corrected for the spatial viewing biases to obtain a measure based purely on low-level image features. The probability of fixating a location, given its local features, was then estimated using Bayes' rule. Finally, the stimulus dependent salience value of each bubble was calculated as the product of the fixation probabilities based on luminance and texture contrast.As a next step we investigated to what degree the fixation of bubbles can be predicted by a spatial bias towards the screen center Based on the three maps, we simulate gaze trajectories of 75 virtual participants in 280 trials each in close analogy to the actual experiments. For each simulated trial, the global stimulus independent fixation map and the stimulus specific bubble mask are combined by point-wise multiplication. This combination results in an intermediate map of the spatial bias specific for the position of the bubbles in the stimulus considered . Next, tFrom the simulated data we obtain a scalar measure for the fixation probability of each bubble, independent of the task and the spatial structure of the respective full field images. Instead, this spatial bias salience is based solely on the spatial position of the bubbles and the global properties of fixation points and saccades.response distribution). If this distribution is flat, the bubble contains no information relevant for classification, and performance of subjects viewing only this bubble would be at chance level. If one of its components is one and all others are zero, then the bubble contains maximal information. This is captured by the entropy of a bubble's response distribution. If I(B) denotes the information content and R(B)P denotes the response distribution of bubble B, with entropy R(B))EP and depends only on the number of degrees of freedom of R(B)P. For tasks expression, gender, influence, and space, maxE is 2, 1, 1, and 1, respectively.To characterize task dependent influences on fixation behavior, we derive a scalar measure for the information a bubble contains with respect to a classification task. First, we assume that each individual bubble is associated with a probability distribution that captures how likely the subjects are to decide for each stimulus class P. To estimate the empirical saliences of single bubbles from measured classification responses to stimuli composed of several bubbles, we need to make an assumption on how the response distributions for single bubbles are related to the joint response distribution of a stimulus containing those bubbles. Here, we assume optimal probabilistic integration of the independent response distributions of single bubbles (p-model). We describe the response distribution R(S)P of a stimulus 1, …, Bn}S = {B by the function Z operating on the individual response distributions R(B1), …, PR(Bn)P.Z the information integration function. It integrates the response distributions of single bubbles independent of the bubbles' absolute position or their relative arrangement. Furthermore, it does not relate to the visual content of the bubbles. It is defined asd, is the appropriate normalization. Z is formally derived by writing the probability for a stimulus 1, …, Bn}S = {B to be of class c, R(S)[c]P, in terms of the corresponding probabilities for the individual bubbles in S to be of class c, R(Bi)[c]P under the assumption that the individual bubbles are independent.Second, along the same lines we assume that our participants' responses to a stimulus expression, gender, influence and space, respectively . This contrasts with 282, 94, 88, and 89 free parameters in the four tasks, equaling the number of bubbles used for stimulus construction in these tasks, multiplied by the number of possible responses minus 1. We solve this over a determined system of non-linear equations by a maximum likelihood method. Details of this fitting procedure are given in For each stimulus, we can formulate an equation like (6). Hence for each task, we can formulate as many equations like (6) as there are stimuli in that task. These equations operate on response distributions. Each equation can, however, be transformed into a set of scalar equations by considering the different components of the response distributions (probabilities for the different classes) separately. This yields 1791, 600, 588, and 585 equations for the tasks We employ pair-wise correlation analyses (Pearson's correlations) to address the net effect of individual predictor variables. To address how well a linear combination of the stimulus dependent salience, the bubble information, and the spatial bias salience of each bubble can explain the attention it attracts, as reflected by the empirical salience values, we employ a multivariate model. Finally, to correlate one predictor with empirical salience while controlling for the effect of all other predictors, we use semi-partial correlations. For example, when we are interested in the correlation of bubble information and empirical salience controlled for the influence of stimulus-based salience and spatial bias salience, we consider the residuals of a multivariate correlation (with intersection) of stimulus-based salience and spatial bias salience with bubble information. These residuals are the differences between the prediction of the multivariate model and the actual bubble information values. We now correlate these residuals with empirical salience. The result is called the semi-partial correlation coefficient of bubble information and empirical salience.For all, the simple pair-wise correlation analysis, the multivariate correlation and the semi-partial correlation analysis, we used the log transform of the predictor variables and the log transform of empirical salience. This standard practice Text S1Description of the computation of bubble information and of other models of information integration.(0.30 MB PDF)Click here for additional data file.Figure S1Distributions of bubble entropy estimates for different initial bubble entropies (see text) for the expression task. Each plot accumulates the data for all initial bubble entropies in a small interval around the displayed value. The distributions were obtained from simulations (see text).(0.20 MB TIF)Click here for additional data file.


Geriatric patients recently discharged from hospital experience increased chance of unplanned readmissions and admission to nursing homes. Several studies have shown that medication-related discrepancies are common. Few studies report unwanted incidents by other factors than medications. In 2002 an ambulatory team (AT) was established within the Department of Geriatrics, St. Olavs University Hospital HF, Trondheim, Norway. The AT monitored the transition of the patients from hospital to home and four weeks after discharge in order to reveal unwanted incidents.The aim of the present study was to describe unwanted incidents registered by the AT among patients discharged from a geriatric evaluation and management unit (GEMU) by character, frequency and stage in the transitional process. Only unwanted incidents with a severity making contact with the primary health care (PHC) necessary were registered.A prospective observational study with patients treated in the GEMU and followed by the AT was performed. Current practice included comprehensive geriatric assessment and management including discharge planning in the GEMU and collaboration with the primary health care on appointments on assistance to be provided after discharge from hospital. Unwanted incidents severe enough to induce contact with the primary health care were registered during the transitional phase and after discharge.118 patients , with mean age 83.2 ± 6.4 years participated. Median Barthel Index at discharge was 18 (interquartile range 16-19) and median Mini Mental Status Examination 24 (interquartile range 21-26). A total of 146 unwanted incidents were registered in 70 (59%) of the patients. Most frequent were unwanted incidents related to drug prescription regime (32%), exchange of information in and between the GEMU and the primary health care (25%) and service or help provided from the PHC (17%).Despite a seemingly well-organised system for transition of patients from the GEMU to their homes, one or more unwanted incidents occurred in most patients during discharge or four weeks post discharge. The study has revealed areas of importance for improving transitional care of geriatric patients. Geriatric patients are often characterised as frail. Frailty is described as age-related physiological vulnerability, reduced homeostatic reserves and reduced capacity to withstand stress. It is associated with increased morbidity, functional decline, nursing home placement and death -3. FrailContinuity of care during transition from hospital to home and post-discharge follow-up is challenging. Several randomized controlled studies have shown positive effects of discharge planning and supporting patients over a limited period after discharge -29. A tiFor many years St. Olav University Hospital has been collaborating with the primary health care (PHC) in the city of Trondheim, Norway in developing a system to improve the quality of transition of hospitalised patients to their homes -39 Figu. As partThis is a prospective observational study. Patients were followed during the hospital stay and four weeks after discharge.Patients were recruited from the city of Trondheim which has about 160.000 inhabitants. The health care system of Norway is public with hospitals financed by the government, while the primary health care including the nursing homes are financed by the municipalities. Assistance from home care nurses is free, but patients are charged for practical help in their home according to their income.The study was run from a 15-bed GEMU as part of the Clinic of Internal Medicine at St. Olav University Hospital in Trondheim, Norway, the study period lasting from November 2005 throughout June 2007. Approximately 80% of the admitted patients lived in their own homes and were referred to the hospital as emergencies caused by acute medical diseases.All patients planned to be discharged from the GEMU to their homes and who got supported discharge from the AT was asked to participate. Patients being discharged to nursing homes, rehabilitation institutions or other hospital departments were not included. For practical reasons AT services were only offered to patients living in the city of Trondheim. Hence, patients from surrounding municipalities were not included.The GEMU patients went through a comprehensive geriatric assessment (CGA) by an interdisciplinary team consisting of physicians, nurses, occupational therapists, physiotherapists and registered nurses. Discharge planning was emphasised and started as early as possible. More details about the CGA are described in a previous publication .A discharge-planning meeting was arranged for all GEMU-patients during the hospital stay. Participating in this meeting were the patient, his closest caregiver(s), the attending physician, nurse and the AT primary contact in the GEMU and participants from the primary health care. During the meeting the primary health care received information on the patient's medical and functional status as assessed in the GEMU and assistance and adjustments necessary after discharge were discussed. At the end of the meeting the primary health care representative wrote a detailed decision on services to be provided by the primary health care after discharge, specifying assistance related to activities of daily living (ADL) and instrumental activities of daily living (IADL) including administration of drugs, services in a day-care centre, inpatient or ambulatory rehabilitation, short- or a long-term stay in nursing homes, physiotherapy, occupational therapy, in-home rehabilitation, food delivery and acquisition of safety-alarms. After this meeting the primary health care representative reported the patient's status and assistance to be provided to the office of home health services (HS) Figure . The serMembers of the AT were of a nurse, an occupational therapist and a physiotherapist, all being experienced with specialised education and training in geriatrics. The physicians in the GEMU served as medical consultants for the AT. The intention of the follow-up by the AT was to cooperate with the primary health care and the GEMU in order to prevent, uncover and resolve unwanted incidents of importance for the patient's situation, e.g. misunderstandings concerning medications, absence of assistance from the primary health care or problems influencing the patients' health condition after discharge.At discharge the attending physician in the GEMU wrote a discharge letter to the patient including information on diagnosis, drug regimen and medical follow-up after discharge, and a copy was sent to the home care nurse. A complete medical report was later sent to the patient's general practitioner. In addition, the GEMU team members sent a case summary to the responsible carer in the primary health care with detailed recommendations on follow-up of e.g. nutrition, physical exercise, adjustments of home environment.After discharge the patients were expected to receive assistance from the primary health care as decided during the stay in the GEMU according to the routines of the home health services. The AT visited the patients during the first week after discharge, while contact afterwards was generally maintained by telephone calls to the patient, the closest caregiver, or representatives from the primary health care, at least once a week during a follow-up period of four weeks. Occasionally representatives from the primary health care contacted the AT. Figure Baseline characteristics were collected from the patient's hospital record, the hospital database and the decision from the discharge-planning meeting.During the first four weeks after discharge the AT team members, registered predefined unwanted incidents on registration forms. Only unwanted incidents with a severity making contact with the primary health care necessary were registered. If uncertainty whether or not it should be defined as an unwanted incident, a consensus was achieved with the project coordinator, the senior medical officer of geriatric medicine in Trondheim and with an experienced geriatric nurse in the GEMU.Cognitive function was assessed during the hospital stay by an occupational therapist, using the Mini Mental State Examination (MMSE) . ADL wasThe GEMU staff members, representatives for the AT as well as the primary health care participated in the planning of the study. Main outcomes of this study were unwanted incidents registered during the hospital stay, at discharge and during four weeks of follow-up. Unwanted incidents to be registered were defined according to a literature review and clinical experience with 230 patients during a running-in period of two and a half year. A standardized instrument was developed, tested out in a pilot study, revised and then implemented as the final registration form. The form was designed to register a diversity of incidents related to the transitional process from hospital to the patients' homes. The unwanted incidents were detected through contact with patients and caregivers and observations made at home visits by the AT. Figure Criteria for registering unwanted incidents were 1) the CGA during the stay in the GEMU turned out to be incomplete, 2) routines for exchange of information between the GEMU and the primary health care were not followed, 3) the drug regimen from the GEMU was not administrated correctly by the home services or the patient, 4) discrepancies in the decision from the discharge-planning meeting and the assistance actually provided by the primary health care, or 5) the guidelines for practical assistance in the patient's homes were not followed.Statistical analyses were performed in SPSS version 14.0. An alpha level of 0.05 was chosen for statistical significance. Differences between included and non-included patients were tested by Independent Samples t-tests for ratio data and Pearson's Chi-Square tests for nominal data. The Wilcoxon signed ranks test was used to test change in Barthel Index score from admission to the GEMU till discharge. Binary logistic regression analysis was used to test whether length of stay, the patient living alone or not, the amount of assistance from the HS and Barthel Index score at discharge, explained the probability of an incident to occur.Participation was voluntary and in accordance to the Helsinki declaration. Those who refused to participate received the same treatment and follow-up from the GEMU, AT and PHC as participating patients. All patients received both oral and written information about the project. The Regional Committee for Ethics in Medical Research and The Norwegian Social Science Data Services approved the protocol.During the study period 170 patients being discharged to their homes were asked to participate, of whom 39 refused, giving a study sample size of 131 patients. Moreover, data from 13 of the enrolled patients' were excluded from analyses because main outcome variables had not been registered. Thus, the analyses involve 118 patients (69.4% of those who were invited to participate). At baseline there were no significant differences in proportion of women (p > 0.3), age (p > 0.8) or independent living (p > 0.6) between enrolled patients and patients refusing to participate, or between patients included and excluded from the analysis .Patient characteristics are described in Table A total of 146 unwanted incidents were registered in 70 (59%) of the patients, giving a median of 2 incidents per patient. Figure The most commonly reported incidents were related to 1) drug prescription regime, exemplified by the home services not replacing the patients' old list of medications with a new one after the hospital stay, or that the patient got the wrong drug or incorrect dosage, 2) exchange of information, e.g. patients were not referred to a specialist as planned before discharge, messages were not passed on, there were shortcomings in the medical report from the attending physician or in case summaries from the nurse, or decisions about the patient's assistance decided in the discharge-planning meeting were not passed on from the representative from the primary health care to the home services, 3) service or help provided from the primary health care, e.g. they did not make sure that the patients ate their meals (as the decision said they should) and 4) decisions made in the discharge-planning meeting in the GEMU were not relevant for the patient after discharge, e.g. the patient needed assistance with the prescription regime after all. The distribution of the unwanted incidents is shown in Table During the follow-up period three of the patients needed more help from the HS, five patients visited the GP in his office and another five were visited by the GP in their home, there were six unplanned readmissions to the hospital and one admission to a nursing home and one patient died.Clinical experience and previous studies have shown that unwanted incidents after discharge of patients from hospital to their homes are common, especially related to discrepancies in drug regimen -12. The Despite specific guidelines and accepted routines for transition from hospital to home, most patients in the present study experienced one or more unwanted incidents. This may have multiple explanations. Firstly, the study group was frail, of high age, often living alone, and having disabilities, including cognitive impairment, making them dependent of assistance in many daily life situations and vulnerable for experiencing incidents.Secondly, although both the GEMU and the primary health care continuously work on improving communication within and between the systems, the large number of helpers around the patient requires that information about the patient is exchanged between different professions and employees several times and thereby increase the likelihood of unwanted incidents.Thirdly, the skills and focus of the GEMU and the primary health care staff were different. The interdisciplinary staff of the GEMU emphasises optimal treatment for disorders precipitating somatic and psychiatric conditions, functional problems and the social situation of relevance for the actual hospital stay, while the main focus for the primary health care is to make it practicable and possible for the patient to stay at home as long as possible. Furthermore, the GEMU staffs was specialized in geriatric medicine while most of the employees in the primary health care had no specialised training in treating the actual patient group, many were unskilled. This may to some extent also explain why the patients did not receive help that was specifically appointed by the primary health care Table .However, the study also revealed that some decisions made in the discharge-planning meeting in the GEMU were not relevant after discharge Table . Such erIn concordance with other studies -11 we foAs shown in Figure It was shown that the patients in the present study during follow-up needed consultations from GPs, new hospital and nursing home admissions and one patient even died. However, the study was not designed to evaluate if these events were consequences of the unwanted incidents registered and thus could be prevented. In a controlled study it would be possible to evaluate if improvement in the transition from hospital to home could improve patient related outcomes.Studies have shown that post-discharge visits in the patient's home by competent professionals can reveal important and potentially reversible clinical problems and unwanted incidents -29 and tStrength of our study was the comprehensive registration of a diversity of incidents related to the transitional process from hospital to the patients' homes and not only disagreements related to drug regimens. Most likely this has resulted in a higher number of incidents than registered in earlier studies. We argue that this broad perspective has given important knowledge about aspects of the patients' situation to further improved discharge planning and follow-up. Though, it is still not known, though to what extent the registered incidents in our study influence the patients' and the caregivers' quality of life and function, hospital readmissions, nursing home placement or death. This needs to be highlighted in future studies.The study has some limitations. Firstly, the practice for the cooperation between GEMU, AT and the primary health care described in the present study is unique for the city of Trondheim and the external validity may be questioned. However, we believe that the challenges observed are recognisable also for frail patients in general when focusing on transitional care. Secondly, we were not able to find standardised questionnaires for registration of relevant unwanted incidents. Therefore an incidence form was developed and revised through a pilot study; however it was not tested for reliability. Filling in the form was based on consensus between the GEMU, the AT and the primary health care which should reduce the possibility for systematic bias. Thirdly, according to the pragmatic design of the study, the AT had a double role being involved both in the planning of the transitional care during the hospital stay, but also in uncovering and resolving unwanted incidents of importance for the patient's situation, as well as registering unwanted incidents. This might have reduced the number of incidents, indicating that the incidence rate could have been even higher than registered without an AT.Despite a seemingly well-organised system for cooperation between the GEMU and the primary health care, unwanted incidents occurred in approximately 60 percent of frail elderly patients during transition from hospital to home and four weeks of follow-up. The majority of unwanted incidents were related to exchange of information, drug regimens and disagreements between services appointed and services provided by the primary health care. The study both demonstrate the need for and the challenges in designing a well-functioning system for this complex patient group both during the hospital stay as well for the time after discharge. The knowledge gained through the study has given insights of importance for future improvements of transitional care for geriatric patients.Further studies, including interventional studies and evaluation in other settings, are necessary to investigate if these results are valid outside Trondheim and if improvements have impact on patient related outcomes such as quality of life, unplanned readmissions and nursing home placement.The authors declare that they have no competing interests.MM, OS, and IS planned the study. MM and TR took part in data collection. MM and JLH have analyzed the data and MM has written the paper in close cooperation with the other authors. All authors contributed to the development and revision of the manuscript and approved the final version.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6963/10/1/prepub


In clinical practice a diagnosis is based on a combination of clinical history, physical examination and additional diagnostic tests. At present, studies on diagnostic research often report the accuracy of tests without taking into account the information already known from history and examination. Due to this lack of information, together with variations in design and quality of studies, conventional meta-analyses based on these studies will not show the accuracy of the tests in real practice. By using individual patient data (IPD) to perform meta-analyses, the accuracy of tests can be assessed in relation to other patient characteristics and allows the development or evaluation of diagnostic algorithms for individual patients.In this study we will examine these potential benefits in four clinical diagnostic problems in the field of gynaecology, obstetrics and reproductive medicine.Based on earlier systematic reviews for each of the four clinical problems, studies are considered for inclusion. The first authors of the included studies will be invited to participate and share their original data. After assessment of validity and completeness the acquired datasets are merged. Based on these data, a series of analyses will be performed, including a systematic comparison of the results of the IPD meta-analysis with those of a conventional meta-analysis, development of multivariable models for clinical history alone and for the combination of history, physical examination and relevant diagnostic tests and development of clinical prediction rules for the individual patients. These will be made accessible for clinicians.The use of IPD meta-analysis will allow evaluating accuracy of diagnostic tests in relation to other relevant information. Ultimately, this could increase the efficiency of the diagnostic work-up, e.g. by reducing the need for invasive tests and/or improving the accuracy of the diagnostic workup. This study will assess whether these benefits of IPD meta-analysis over conventional meta-analysis can be exploited and will provide a framework for future IPD meta-analyses in diagnostic and prognostic research. Ancient Egyptian medical papyri 1550 BC) already emphasised diagnosis by physical examination as the cornerstone of the decision to treat or not to treat an ailment [550 BC alStudies on primary diagnostic research typically examine the accuracy of a test isolated from history and clinical examination or do not adjust for overlap of information captured by clinical history, physical examination and additional tests. Such studies and conventional meta-analyses of their reported results will therefore not show how useful the test will be in practice -4.In addition to the predominance of isolated, single test evaluations in published literature, variations in design and quality of studies on diagnostic topics -8 make tDue to the limited space in medical journals and the lack of standard procedures to make original data accessible, little empirical evidence is available about the influence of many patient and study characteristics  on the estimates of diagnostic performance of tests ,21.Another limitation is the fact that many original reports of diagnostic and prognostic meta-analyses report data only in a dichotomous way, since many test results that are continuous in nature are classified as abnormal or normal. By doing so, these meta-analyses are based on reduced information, thus neglecting the potential diagnostic information contained in continuous test results. They possibly give an overestimation of the accuracy by selection of optimal cut-off values in the original studies ,22-24.As a consequence, it is difficult to make a good assessment of the generalisability of the accuracy of tests, either in an isolated situation as well in the context of other tests.In contrast with conventional meta-analysis of test accuracy studies, individual patient data (IPD) meta-analysis has the potential to establish the value of test combinations. First, in IPD meta-analysis test results can be analysed taking into account the continuous test results rather than the dichotomous classification that is generally used in reports of diagnostic and prognostic tests. The use of original continuous data instead of the dichotomized reported test results creates the possibility to detect a  relation between test result and disease and it makes it possible to estimate test accuracy at different cut-off values. Second, the additional information provided by diagnostic tests can be examined in light of the diagnostic information already known from history and clinical examination, and less expensive or less invasive tests ,22,25-28Assumptions about invariance of test accuracy across a range of disease prevalences (prior probabilities) can be tested. Finally, also the association across patient-level characteristics or between patient level and study level characteristics  can be assessed, without the ecological fallacy problem.To our knowledge, no IPD meta-analyses of diagnostic or prognostic research have been conducted so far. In this paper we describe the outline of a research program to systematically evaluate the potential benefits of IPD meta-analyses in the evaluation of diagnostic tests. Thereby, we selected four clinical problems from gynaecology, obstetrics and reproductive medicine that will be used as clinical cases for this methodological project:1. Diagnosis of endometrial cancer in women with postmenopausal bleeding (PMB)2. Prediction of preterm birth3. Diagnosis of tubal pathology in subfertile women4. Assessment of ovarian response in women undergoing in vitro fertilisation (IVF)The objectives and research methods will be outlined below, and practical, methodological and clinical issues that we anticipate to encounter will be discussed.The major goal of this study will be the development of prediction rules and diagnostic algorithms for individual patients. We will create these rules and algorithms by performing IPD meta-analysis on the four clinical problems mentioned above. Within this major goal, we address both methodological as well as clinical objectives.First, we aim to contribute to the development of a framework for performing IPD meta-analyses and to provide practical and methodological recommendations on how to perform an IPD meta-analysis in diagnostic and prognostic research.Second, we will attempt to gain a better understanding of sources of heterogeneity between studies and to explore the role of missing values in this type of meta-analysis.Finally, we aim to compare IPD analyses with those based on aggregated data in conventional meta-analyses, to explore when the IPD approach is beneficial, and when a conventional approach suffices for reliable and unbiased estimates of diagnostic/prognostic accuracy.The clinical objective of the project is to create optimal diagnostic and prognostic strategies, incorporating probabilistic models for the individual patient profile and make them available to clinicians in ways that allow their practical integration with clinical practice.With the help of IPD meta-analysis we aim to re-analyse the estimates of diagnostic or prognostic accuracy of tests in their clinical context and for different subgroups and compare them to estimates resulting from a more conventional meta-analytic approach.Assuming that taking into account relevant patient and clinical history characteristics together with physical examination and several tests, by using probabilistic models, improves the accuracy and efficiency of the diagnostic work-up, this probabilistic approach could be used to improve clinical practice.In addition, current guidelines for the management for each of the four clinical examples will be adjusted to reflect the results of this study and to provide support for using probabilistic models in the clinical setting.Prediction rules and diagnostic algorithms will be developed for each of the four clinical problems:Post-menopausal bleeding (PMB) accounts for a large proportion of gynaecological consultations in both primary and secondary care . In mostPreterm birth occurs in 7% of all deliveries – 15.000 cases per year in the Netherlands – and accounts for 70% of perinatal mortality and 40% of severe cerebral morbidity . Many reIn the United States, about 8% of all women between 15 and 44 years are suffering from subfertility . In the Multiple tests for the evaluation of tubal patency exist, of which the most commonly used are Chlamydia Antibody Tests (CAT), hysterosalpingography (HSG) and diagnostic laparoscopy with chromopertubation, the latter often being considered a gold standard test.At the moment, there is no consensus on which test should be initially used in the diagnostic work-up, or on the most effective and cost-effective sequence of tests.By using IPD meta-analysis we will integrate patient characteristics and results of diagnostic tests for individual patients with subfertility and assess various combinations and sequences of tests.Around 15.000 IVF/ICSI cycles are performed each year in the Netherlands. The most important single factor to determine success is maternal age. Age related decline of success is largely attributable to a progressive decrease of oocyte quality and quantity with increasing female age. Over the past two decades a number of ovarian reserve tests have been designed and evaluated for their ability to predict outcome of IVF in terms of oocyte yield and occurrence of pregnancy . Many ofPreviously, systematic reviews of studies on diagnostic and prognostic test accuracy for each of the four clinical topics were performed and by means of these reviews we identified the relevant primary research in these four areas ,40,43-47We aim to include datasets from all studies meeting the inclusion criteria of the original (updated) reviews. Studies that have met the inclusion criteria in the meta-analyses on postmenopausal bleeding were prospective studies that reported on endometrial thickness and in which the transvaginal ultrasound was performed before tissue assessment. The selection criteria for the meta-analyses on preterm birth were studies on asymptomatic or symptomatic pregnant women, cervicovaginal fetal fibronectin testing before 37 weeks' gestation, known gestation at spontaneous birth and observational cohort design. The meta-analyses on tubal pathology included studies that compared CAT or HSG to laparoscopy for tubal pathology and that described a clear distinction between tubal occlusion and peritubal adhesions. For the ovarian reserve tests meta-analyses included studies that reported on the association of follicle stimulating hormone (FSH), anti-mullerian hormone (AMH), antral follicle count (AFC), ovarian volume or clomiphene citrate challenge test (CCCT) with poor ovarian response or pregnancy after IVF. All meta-analyses only included studies with sufficient data to construct 2 × 2 tables. Exclusion criteria were a lack of binary data for constructing the 2 × 2 tables and inadequate study quality. Study quality was defined as a clear description of sampling, data collection, study design, blinding,  verification and missing data. Adequate test description and description of either the population or the reference test was also included in the assessment of study quality ,43-47.Including all available studies in our IPD meta-analyses will maximise our ability to study the factors associated with heterogeneity in model intercepts and coefficients and diagnostic odds ratios. We will therefore also consider studies that have potentially collected relevant data, but that have been excluded in previous analyses.We will approach all authors of the selected original studies to inform them about this IPD meta-analysis project and invite them to share their data in this collaborative project. If they are inclined to participate, they are provided with a more detailed study proposal, and asked to send their original datasets. We ask them to send the complete database as to minimise their efforts going through their dataset to select the appropriate variables. Any data format is accepted, provided that variables and categories are adequately labelled within the dataset or with a separate data dictionary. We aim to include datasets from all studies meeting our target variables as described in table We will define study quality of the original studies to a large extent in the same way as it was described in the systematic reviews (see above under the heading 'data acquisition'). We will report the study quality according to the STARD statement . CompletQuality of the received data will be judged by the assessment of consistency of the data and the published manuscript. We will also assess reproducibility of the reported accuracy in the manuscript using the raw data. By requesting the original research protocols we will be able to create an overview of included patients and test sequences, which might be used to explain the heterogeneity between included studies. We also will perform thorough data checks . The original investigators will be contacted to confirm missing data or to check values of doubtful validity. In addition to this, further details during discussions with primary investigators at a collaborators meeting, may shed light on specific problems encountered during study execution, and resolve differences due to the use of different definitions. Such discussions may give us more precise descriptions of the test procedures used, and the proficiency of the examiners, if the protocols were unclear on these points.Unfortunately some data may have to be excluded from the IPD meta-analyses due to incomplete data or major inconsistencies with published results. Data are only considered to be incomplete when a substantial part of the most relevant variables was not available in the original study and the original authors are not able to provide the missing data. We emphasise that a valid diagnostic model can be derived based on fewer than all available data sets.After the assessment of study and data quality the variable codes of all the acquired data will be compared between the original databases. If the variables are compatible the original data will be merged and a study identification variable will be added to reflect the stratified nature of the pooled dataset. Within this database we will create subgroups on all relevant issues concerning the clinical problems  .This will be followed by fitting univariable models. Subsequently, multivariable regression models will be created, both for clinical history and examination alone, as well as for various combinations and sequences of relevant patient characteristics with additional tests. This will finally lead to the development of the individual diagnostic or prognostic algorithm. We will use imputation strategies that we have applied previously for missing data at the individual level. For missing data at the study level (i.e. information not documented in a study), we will also consider imputation to allow multivariable analyses on the most complete dataset, although the added value of such major imputation efforts may be limited and will be explored in the perspective of IPD meta-analysis . The mulTo compare the results of the included studies in the IPD meta-analysis approach we will also perform a conventional diagnostic meta-analysis for the same set of studies. As this work has in part already been performed ,40,43-47To adjust for overfitting, we plan to use several internal validation techniques (bootstrap (patient level), leave one out (study-level)) . We inteThe analyses described above will be assessed for all four clinical topics. For an outline of the individual assessments of the topics see the following part and table Data collected will contain patient characteristics and tests as described in table Since it has been reported previously that the accuracy of endometrial thickness measurement is different in obese and non-obese women and in diabetic and non-diabetic women , differeFinally, three different diagnostic decision rules based on these two models will be explored in terms of diagnostic efficiency, and compared to current clinical practice . The three evaluated strategies will be(1) the "patient characteristics" rule, i.e. probability estimates based on patient characteristics, and invasive diagnostics in case the probability of (pre) malignancy is over 3%. In this decision rule TVS is not performed.(2) "selective" rule, i.e. probability estimates based on patient characteristics, TVS in case the probability for cancer exceeds 3%, and subsequent histological analyses when the endometrial thickness exceeds 4 mm.(3) "integrated" rule, i.e. TVS in all patients, with a probability estimate based on both patient characteristics and TVS results, completed by endometrial sampling when the probability of cancer exceeds 3%.Data collected will contain patient characteristics and tests as described in table We will use several outcome measures, including the condition of the child. However, for the purpose of the present study, delivery prior to 32 weeks will be the primary outcome. We will look at the distribution of several characteristics, including cervical length. Subsequently, we will perform receiver-operating characteristic analysis for cervical length, as well as other continuous tests. We will build two multivariable models to predict preterm birth. The first model will be based on patient characteristics only ("patient characteristics model"). In the second model, patient characteristics will be combined with cervical length and fibronectin. We plan to combine the diagnostic data with data from the effectiveness of progesterone in the prevention of preterm birth, as the latter agent has found to be effective in the prevention of preterm birth in women with a previous preterm delivery . By doinData collected will contain patient characteristics and tests as described in table Presence of tubal pathology will be the primary outcome measure. We will perform all analysis twice. In the first analysis, tubal pathology will be defined as two-sided tubal occlusion. In the second analysis, tubal pathology will be defined as any form of tubal occlusion, be it one-sided or two-sided. We will perform ROC-analyses for continuous variables, such as age and CAT. Subsequently, univariable logistic regression analysis will be performed. This analysis will continue on an analysis that we have performed previously . Again, Data collected will contain patient characteristics and tests as described in table For the analyses on the ovarian reserve tests we will use two outcome measures; ovarian response and pregnancy. The exact definition of these two outcome measures will depend on the available data and on the outcome of the discussion at the initiating collaborative work-shop. Variables considered are shown in table We have previously published a decision analysis in which we integrated patient valuations of subfertile couples (incorrect withholding of IVF versus undergoing IVF without success) and predicted probabilities of IVF-success. This analysis revealed a so-called threshold ROC-curve, which showed the minimal accuracy that an ovarian test (or combination of tests) should have to be of clinical value . We willhttp://www.ipd-meta-analysis.com/ipd. The website will contain protocols, including the description of the objectives of each project and proposals for the statistical analyses. Moreover, the diagnostic algorithms that will be the result of the project will be available from the website after the studies have been completed.We have developed a website with information on the progress of the project. See The clinical "end products" of these IPD meta-analyses will be prediction rules for each of the four clinical problems: women with PMB, women at risk for preterm birth, women suspected of having tubal pathology, and women starting with IVF. The results will be made available through simple scoring chards as well as logistic regression models. The latter will become accessible through web applications at which doctors can enter relevant data of the patient. Furthermore, such prediction rules will be made available for patients, as we did previously with prediction rules developed for spontaneous pregnancy in subfertile couples . We willWorkshops will be organised with all investigators of the included studies. In addition to discussing the IPD-meta-analysis project in general, as well as the practical, methodological and data-related aspects of each original study, these meetings are also important to build trust. During these workshops, we will discuss and refine the study protocol, examine patient characteristics and information from diagnostic tests that are to be analysed, the data checking procedures and the main analyses to be performed. Criteria for classifying test results, including results of reference tests, as positive or negative will also be discussed, taking into account that the exact nature of tests and procedures will differ between studies and centres. We will also propose a timetable and a publication policy, including a list of anticipated publications, with a collaborative group authorship for these publications, to be discussed and agreed upon by all collaborating authors.The results from the IPD meta-analysis will be presented at a collaborators meeting. Any subsequent articles on the results of the meta-analysis will be published under the name of the collaborative group. It will also be circulated to the collaborators for comments, amendments and approval before finally being submitted. In the case of any disagreement, the following fundamental principle will be applied; the report should provide the meta-analysis results, presenting all of the available evidence, but will not include any interpretations of the data, except those that are unanimously decided upon by all collaborators. Any collaborating group is free to withdraw its data at any stage.Although it is at present stage not possible to exactly anticipate on the clinical and methodological results from the planned steps in each of the four clinical topics, we expect to have the following knowledge available at the end of the project:Methodological knowledge:• Differences between conventional meta-analyses with summary estimates of sensitivities, specificities and ROC-curves, and IPD meta-analyses.• Knowledge of quality of reporting on individual studies• Knowledge of completeness of data and ways to deal with missing values• Knowledge of differences and similarities in distributions of parameters between studiesClinical knowledge:• Prediction models and diagnostic models obtained with IPD meta-analyses and the relative performance in comparison to aggregate meta-analyses• Estimates of accuracy and calibration of the prediction models and diagnostic models• Integration of the diagnostic and prognostic knowledge with knowledge of therapeutic effectivenessIncreased efficiency of the diagnostic work-up by making optimal use of the patient characteristics combined with the results of the diagnostic tests, will probably decline the need of invasive tests and contributes to improved patient care. With help of the results of the four clinical problems, we can then assess the potential value of IPD meta-analysis in diagnostic and prognostic models, compared to conventional diagnostic meta-analysis.From the experiences in the present proposal, we will provide recommendations on how to perform IPD meta-analysis in prognostic and diagnostic research.http://www.ipd-meta-analysis.com/ipd.A final step in the work-plan is to provide these data through the internet. The progress of the project can be followed on The authors declare that they have no competing interests.BM is the principal investigator of the study described in this article. BM, LB, KK and GR developed the initial study protocol. KB and BO participated in the study design and coordination. KB wrote the first draft of the manuscript. All other authors commented on this draft and contributed to the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2288/9/22/prepub


Drosophila experiment. The checkerboard strategy provides a technique for the manipulation of migration rates  in order to affect the persistence of a metapopulation. It may be used in order to minimize the risk of extinction of an endangered species, or to maximize the efficiency of an eradication campaign.The persistence of a spatially structured population is determined by the rate of dispersal among habitat patches. If the local dynamic at the subpopulation level is extinction-prone, the system viability is maximal at intermediate connectivity where recolonization is allowed, but full synchronization that enables correlated extinction is forbidden. Here we developed and used an algorithm for agent-based simulations in order to study the persistence of a stochastic metapopulation. The effect of noise is shown to be dramatic, and the dynamics of the spatial population differs substantially from the predictions of deterministic models. This has been validated for the stochastic versions of the logistic map, the Ricker map and the Nicholson-Bailey host-parasitoid system. To analyze the possibility of extinction, previous studies were focused on the attractiveness (Lyapunov exponent) of stable solutions and the structure of their basin of attraction . Our results suggest that these features are of secondary importance in the presence of stochasticity. Instead, optimal sustainability is achieved when decoherence is maximal. Individual-based simulations of metapopulations of different sizes, dimensions and noise types, show that the system's lifetime peaks when it displays checkerboard spatial patterns. This conclusion is supported by the results of a recently published  No one can produce all his needs by himself. Personal autarky poses a serious danger of collapse in cases of illness, drought, etc. Trade reduces the impact of local catastrophes, thus increasing economic stability. However, the recent series of econo-crises revealed that globalization induces coherence among markets and jeopardizes their sustainability against global failures. Economists try to identify the optimal tariff that balances between the dangers of autarky and the risk of correlated failure. The same problem appears in ecosystems with a population divided among local habitat patches. “Optimal tariff” is translated to optimal migration rate: how should one manipulate connectivity among patches in order to achieve maximum sustainability? Recolonization of habitats that undergo extinction is essential for survival, yet a too strong dispersal leads to coherence and correlated extinction. Here we use individual-based models in order to find the optimal migration rate. We show that this optimum appears when the the system takes a spatial “checkerboard” pattern that maximizes the decoherence. The insights gleaned allow for improved policies for conservation of endangered species  and, on the other hand, eradication campaigns (like vaccination or pest control). In recent years, many studies in the field of biodiversity maintenance were focused on spatially structured populations The population of an isolated patch is usually unstable, as demographic and environmental fluctuations may drive the colony to extinction. Migration among subpopulations allows recolonization of vacant habitat patches (turnover events) and reduces the risk of correlated extinction The situation becomes much more complicated if the local dynamics of a large, well-mixed population is also extinction-prone. In such a case, strong dispersal, which is equivalent to patch merging, increases spatial coherence and leads to global extinction. Many recent experiments on predator-prey maximum sustainability such that the chance of extinction is minimal. For this purpose, we have developed a numerical technique that allows one to consider the effect of demographic stochasticity and the possibility of extinction for a spatially structured population. To demonstrate the scope of our results, we have considered first the most studied system in the field, namely, the logistic map, and then two other paradigmatic systems: the Ricker map and the well-known Nicholson-Bailey host-parasitoid dynamics.There is a substantial literature on the two edges of the bell shape: the extinction transition that takes place as migration becomes too small checkerboard pattern. In the following section, we show that the maximal persistence time appears when the decoherence peaked, as this is the underlying mechanism beyond stability. The three different systems  are analyzed in detail, and we demonstrate consistently that in each of them the maximum sustainability is associated with a checkerboard pattern.Our main result is the identification of the conditions for maximum sustainability. Surprisingly, it turns out that the optimal point for the stochastic system has nothing to do with the stability properties of the deterministic (noise-free) dynamics. Instead, it always appears when the spatial system arranges itself in a Along this paper we deal solely with demographic stochasticity. However, it should be emphasized that our results hold in the presence of other types of noise, like the environmental stochasticity considered by First let us present the numerical technique used in order to study the effect of demographic stochasticity on the sustainability of a spatially segregated population. We demonstrate this technique for the logistic system; the generalization of this method to any other dynamics is presented in the We consider a metapopulation with on averageEach of the Indeed, The deterministic limit of the stochastic-logistic system corresponds, thus, to the paradigmatic model of diffusively coupled logistic maps, considered already in the context of population dynamics per se may support chaotic dynamics where the population assumes an arbitrarily small value, but it never allows for extinction. To consider the possibility of extinction, one must adds demographic stochasticity to the model, i.e, use (1) instead of (2). For a small system, the average time to extinction Note that this deterministic model attributed to the features of the spatial deterministic dynamics, namely, to the existence of an attractive manifold? Not really. Let us take a look at Does this fact indicate that the peak should be These individual particle simulations show that for realistic values of ions see .The fractal basin boundary and the dependence of the asymptotic behavior on initial conditions have already been pointed out by Adler In the NB case we can show the orbit diagram for only a limited region of the phase space, where generic initial conditions converge to an attractive orbit, a phenomenon first pointed out by Adler In both the Ricker and the Nicholson-Baily maps, the system achieves maximum sustainability in the “UDUD” region, similarly to the case of the logistic map. Here too, the value of the Lyapunov exponent turns out to be irrelevant for determining the maximum sustainabilty point.The exceptional stability of the checkerboard pattern has to do with the fact that in this state the decoherence among neighboring habitat patches is maximal. To understand this we briefly review some elements of previous studies.A generic mechanism that leads to sustainability in spatially structured populations has been discovered recently if the oscillations on these two patches are incoherent, then migration between patches drives the whole system inward toward the coexistence fixed point, yielding sustained oscillations. However, one should bear in mind that dispersal itself tends to reduce population gradients and induces synchronization. In order to gain stability, the migration among patches should be weak enough to allow for noise-induced desynchronization, yet strong enough to stabilize incoherent patches. As discussed in In order to grasp the essence of the stabilizing mechanism, let us look at Stable orbits, thus, may appear due to the presence of noise. The role of noise is to perturb the system from its fully synchronized phase. Once this perturbation happens, the differences between patches are amplified by the underlying unstable dynamic. This yields an effective decoherence between patches and, as a result, the dynamic stabilizes.Based on this insight, we suggest that the optimal persistence is always achieved at the point of maximum decoherence. The basic unit is a two-patch model in the “up-down” phase, and the whole system should be tiled with these dominoes in a checkerboard array that allows for maximum rescue effect. As demonstrated in As shown above, the very same result holds for different dynamics that acquire stability through spatial structure, like the Ricker map considered by Drosophila experiment The results of the All these considerations fail when the number of individuals per site becomes extremely large (the system follows the deterministic dynamics and the stability of an orbit is governed by the Lyapunov exponent) or small . These limits are discussed below.Within the general framework suggested by Earn, Levin and Rohani The checkerboard solution manifests itself even if the topology of the system does not allow for “perfect” partition, as in the case of an odd number of patches or an imperfect lattice. As demonstrated in the The numerical procedure used along this work is a generic individual-based generalization of the deterministic approach for coupled map lattice A very similar island model has been used by Hamilton and May On the contrary, here we consider the case where in the deterministic limit, the dynamics are unstable (chaotic or otherwise extinction-prone), and thus stochasticity induces fluctuations, and their interference with the spatial structure plays a crucial role in the persistence of the population.We assume a population dynamics with nonoverlapping generations, where any generation involves two consecutive steps. The first step involves the “local reaction” , and the second is the density independent “migration”  step, where individuals are allowed, with a certain probability, to leave their local community and migrate to another patch. No “dispersal cost” is introduced, so any emigrant reaches its chosen destination.In the reaction step, the number of agents on a patch at the Both maps are chaotic, and after a while the system reaches population levels that are very close to zero. The deterministic formalism has no problems with that: since After the reaction step, a migration step takes place. In the deterministic limit, a fraction Another example we consider here is the non-chaotic (yet extinction-prone) Nicholson-Bailey Along this work we have used three procedures in order to estimate  in  see . This haIf the system reaches extinction on reasonable timescales, we can simply average over the time to extinction obtained from repeated runs of the simulation with random initial conditions and different histories. The result is To implement this method, one assumes that the population fluctuates, more or less normally, around rbit see  above. THere we did not use the stochastic simulations at all. Instead, it is assumed that the demographic fluctuations effectively kicked the system away from the stable orbit, into a random location  in the phase space. This chosen point becomes a new “initial condition” that flows, in the deterministic limit, back to the attractive orbit along a transient trajectory. The chance of extinction during this transient is proportional to the minimum over time of the total population, In There are two extreme cases of a too large and too small noise, where the checkerboard strategy fails. In the weak noise limit Click here for additional data file.Video S1As the migration parameter D increases, the fractal map showing Xm for any possible initial condition of a two patch system is changes (lower right). In the lower left panel the orbit diagram is updated for any given D. the upper panel shows the average of Xm over all possible initial states; it yields the bell-shape with the peak at the optimal sustainability point, as explained in the text.(8.58 MB MPG)Click here for additional data file.Video S2The individual based Ricker dynamic is simulated on a 10×10 lattice (periodic boundary conditions), with a “defect”  in the middle. The movie present consecutive snapshots of the density of particles, color coded as indicated by the color bar, at the optimal migration point. One realizes that the system reaches the checkerboard state, with a single moving imperfection localized close to the defect.(5.82 MB MPG)Click here for additional data file.