Isodose lines were generated and dosimetric indices, V150Prostate, D90Prostate, V150Urethra, V125Urethra, V120Urethra, V100Urethra, and D10Urethra were calculated. The algorithm allows for the generation of single‐isotope, multi‐activity hybrid brachytherapy plans. By dealing with only one radionuclide, but of different activity, the biology is unchanged from a standard plan. All V100Prostate were within 2.3 percentage points for every plan and always above the clinically desirable 95%. All V150Urethra were identically zero, and V120Urethra is always below the clinically acceptable value of 1.0 cm3. Clinical optimization times for the hybrid plans are still under one minute, for most cases. It is possible to generate clinically advantageous brachytherapy plans  while incorporating leftover seeds from a previous patient treatment. This method will allow a clinic to continue to provide excellent patient care, but at a reduced cost. Multi‐activity hybrid plans were equal in quality (as measured by the standard dosimetric indices) to plans with seeds of a single activity. Despite the expanded search space, optimization times for these studies were still under two minutes on a modern day laptop and can be reduced to below one minute in a clinical setting. With the typical cost of a set of PPI seeds on the order of thousands of dollars, it is possible to reduce the cost of brachytherapy treatments by allowing for easier use of seeds left over from a previous patient or unused due to a cancelled treatment.The purpose is to demonstrate the ability to generate clinically acceptable prostate permanent seed implant plans using two seed types which are identical except for their activity. The IPSA inverse planning algorithms were modified to include multiple dose matrices for the calculation of dose from different sources, and a selection algorithm was implemented to allow for the swapping of source type at any given source position. Five previously treated patients with a range of prostate volumes from PACS number: 87.55.D‐, 87.55.Kd, 87.55.ne For seeds like  103Pd and  131Cs, with half lives of 17 and 10 days respectively, the decrease is even greater (Current permanent prostate implant brachytherapy (PPI) dose plans use one type of radioactive seed per treatment plan, all with the same activity. Radioactive seeds must be ordered from a vendor and used before the radioactivity dissipates. The half‐life of one of the more common radionuclides,  greater . Thus, iIn addition, it has been reported that it is important to account for differences in the seeds supplied by different manufacturers when generating treatment plans and calculating dosimetry. Identical plans using the same radionuclide but from different vendors will have different dosimetry due to different dose rate constants, anisotropy factors, and radial dose functions of each vendor's seeds.,,3 192Ir seeds for PPI, Glasgow et al. proposed that combinations of  192Ir and  125I seeds can provide adequate coverage of the prostate and sparing of the organs at risk. In this case, the argument is that  192Ir and  125I have similar half lives (73.83 days verses 59.40 days) and similar absorbed doses delivered in the permanent implant. Therefore, the radiobiological effects of using two different radionuclides is negligible for cases which used up to 40 mGy⋅m2/h(10 mCi) 192Ir. In 2007, Chaswal et al. expanded on the forward planning‐based work of Glasgow and used a Greedy Heuristic algorithm to optimize the treatment plans. They showed that there can be dosimetric and trauma‐reducing benefits to using plans with a combination of  192Ir and  125I. These studies are promising in that they show a clinical benefit can be derived from including more than one radionuclide in a given brachytherapy plan. However, the results are dependent on knowing and understanding the biology of the superposition of two different radiation delivery mechanisms.There has been recent interest in generating dose plans with two different radionuclides. In their 2002 paper on the feasibility of ow et al.4 prop 125I and  103Pd. However, even setting aside the uncertainty in CT‐reconstructed target volumes, there is a lack of consensus about the actual value of the biological parameters that govern the definition of the BED, EUD, and TCP, with values of α/β differing by more than a factor of 2. The uncertainty in the models that predict the biological effect of the specific characteristics naturally propagates to the dosimetry of brachytherapy plans that incorporate multiple radionuclides.There has been progress made recently in our understanding of the biological responses to different dose delivery characteristics of different radionuclides. In fact, some recent studies have shown no significant difference between biochemical failure and control as a function of biological equivalent dose (BED), equivalent uniform dose (EUD), or tumor control probability (TCP) when comparing plans using d  103Pd.6 Howe In this work, we aim to avoid the biology‐based uncertainties attached to the use of two different radionuclides. It would be beneficial to be able to generate clinically‐acceptable hybrid plans that incorporate seeds of the same radionuclide, but with different characteristics — be they different activity or slightly different dosimetric geometry. In contrast to plans using multiple radionuclides, single radionuclide hybrid plans are free from the biological uncertainties associated with the efficacy combining characteristically different radiation.However, assuming the uncertainty in the biology is acceptable, there does appear to be a benefit to using multiple types of seeds (each with a different dosimetry) in one brachytherapy plan.,5 InWe present an algorithm that allows for the generation of single‐isotope, multi‐activity hybrid brachytherapy plans. By dealing with only one radionuclide, but of different activity, it is possible to generate clinically advantageous brachytherapy plans: i.e. obtain the same quality dose distribution as a standard single‐activity plan while incorporating left over seeds from a previous patient treatment. This method will allow a clinic to continue to provide excellent patient care, but at a reduced cost. While the core optimization engine remains unchanged, the code was modified in three ways: (1) more than one radionuclide source can be specified using the TG‐43 formalism; (2) the system for evaluating the dose delivered to the target and surrounding organs was expanded to incorporate two separate radionuclide dose profiles; and (3) the optimization engine can switch the seed type at each source position at specific moments during the iteration process.The algorithms developed for this work augment the IPSA inverse planning algorithms.,,, WhDj, delivered to unit volume (voxel) j of an organ, the contributions from the ith source position, Dij, are summed:As in the original algorithm, to determine the dose, Np is the number of source positions. The total dose, D, to an organ is the sum of the dose at each voxel within that organ, Nv is the number of voxels in the organ.Dij) used depends on which seed is present at position i. During the initialization phase of the optimization process, the algorithm computes a separate dose matrix for each seed type. Then an initial configuration of seeds is placed in the target. This configuration is random, but the user has the ability to specify a desired number of each seed type. The user also has the ability to permanently set the seed type at any source position.However, in the new algorithm, the dose matrix  evaluated to determine the value of the objective function and Pouliot. This class solution was shown to mimic an experienced dosimetrist by consistently producing dose plans equivalent (as measured by common dosimetric indices) to those created by a dosimetrist in our clinic. This allows for better isolation of the variables which control the use of partial‐activity seeds in the optimization. Two limits on the number of needles allowed per plan are included: a soft penalty and an upper limit. The soft penalty is a weight multiplied by the total number of needles and then added into the objective function of the optimization. The upper limit restricts the number of needles allowed in one implant. Throughout the studies in this paper, the upper limit is set to 30. This is the limit our clinic imposes, since our clinical experience has led to the conventional wisdom that, as the number of needles in one implant approaches 30, the complications due to edema, erectile dysfunction and other trauma begin to have unacceptably deleterious effects on the post‐treatment quality of life of the patient. On average our clinic uses 25 needles per treatment plan for a 40 cm3 prostate gland.In order to maximize clinical relevance, the optimization parameters were chosen from the class solution developed in Lessard et al.8 and  have demonstrated that the minimum dose delivered to at least 90% of the gland (D90Prostate) had a critical impact on the subsequent risk of prostate‐specific antigen‐diagnosed recurrence. The American Brachytherapy Society has published general guidelines for different anatomical sites that consist of a set of dose limit specifications. Our clinic incorporates these recommendations and imposes even stricter limits for the target coverage and urethra dose, which have yielded reproducible results: mature five‐year biochemical control rates of 96% (median follow‐up 63 months) reported for 118 consecutive patients. Plan quality was evaluated by the UCSF clinical physicist with experience generating over 1500 plans. For the purposes of this study, the single‐activity plans were considered the control sample against which the dosimetry of the multiple‐activity plans was judged. The validity of the standard (single‐activity) plans was examined and established in previous work.The quality of the plans generated in this analysis will be graded using the standard dosimetric indices developed by the American Association of Physicists in Medicine (AAPM), the American Brachytherapy Society (ABS), and our clinic. For brachytherapy treatment of prostate cancer, Stock et al.14 havV100Prostate), at least 150% of the prescription dose (V150Prostate), and the minimum dose that covers 90% of the prostate (D90Prostate). In general, we strive to achieve V100Prostate>95% and V150Prostate<65%. A V100Prostate above 95% ensures complete coverage of the target, but will encourage a value of D90Prostate 10%–20% above the prescription dose. Because this is a not a post‐implant dosimetry analysis, but rather a pre‐implant dosimetry analysis, it is important to see the D90Prostate higher than the prescription dose by approximately this amount, since the dosimetry generally sees a decrease in post‐implant D90Prostate with respect to the pre‐implant D90Prostate.The target coverage was compared across plans by examining volume of the prostate receiving at least the prescription dose  as well as the minimum dose delivered to 10% of the urethra (D10Urethra), can be used to monitor the dose delivered to the urethra. To adequately protect the urethra, we strive to obtain V125Urethra<1.0 cm3 and V120Urethra<1.0 cm3.One of the most common side effects of prostate brachytherapy  is caused by excessive dose delivered to the urethra. The urethral volume receiving at least 100%, 120%, 125%, and 150% of the presection dose 125I seeds (A0/A0=62.5%). In Scenario II, we generate hybrid plans using 0.64 and 0.76 mGy.m2/h(0.50 and 0.60 mCi)125I seeds A/A0=83.3%. Five previously treated patients with a range of prostate volumes from 20 to e common.17 SinUni‐activity–full activity: Only one seed type (the full activity) is available to theoptimization30 partial activity: The optimization is requested to use 30 of the partial activity seeds plus any number of the full activity seeds60 partial activity: The optimization is requested to use 60 of the partial activity seeds plus any number of the full activity seedsUni‐activity–partial activity: Only one seed  available to the optimizationUnrestricted mix (pure hybrid): Allow the optimization to employ any number of each seed typeFor each study, five plan types were generated with a prescription dose of 144 Gy:The soft needle penalty encourages the optimization to minimize the number of needles in addition to optimizing the dose distribution. For Study 1 in Scenario I and II, this needle penalty is set to the value used in our clinic. For Study 2 in Scenario I and II, this was removed and the optimization was run with no per‐needle penalty.V100Prostate, V150Prostate, and D90Prostate) and for the urethra . VUrethra150 is identically zero for all cases and is, therefore, not listed in the Tables.The dosimetric results for Scenario I are presented in V100Prostate) is within 2.3 percentage points for every plan and always above the clinically desirable 95%. The D90Prostate for all plans hovers around 120% of the prescription dose. This is to be expected when evaluating the preplan dosimetry as opposed to the post‐plan dosimetry.Note that for any one case in Scenario I, Study 1 , the tarosimetry.,3V120Urethra is always below the clinically acceptable value of 1.0 cm3. Study 2 under this scenario shows the effect of removing the per‐needle penalty compared to Study 1, which preserves it. Note that the effect of removing the needle penalty is to increase the number of needles and obtain a slight increase in target coverage. Comparing Also note that the The results are similar in Scenario II for both Study 1  and StudStudies were performed on the geometric distribution of each seed type. The density of seeds as a function of distance from the center of mass of the prostate was examined. The prostate was divided in octants with the origin placed at the center of the prostateA final study was performed in order to determine whether the optimized configuration of seed types is independent of the initial configuration. The initial configuration of seed types was set to be all of one activity or the other . The plans were then optimized requesting an unrestricted mix of the two seed types. The final configuration was found to have no correlation to the initial configuration of seed types. optimization for a single‐activity varies from 51–155 s. The hybrid optimization incorporates an increase in the degrees of freedom that must be probed, so an increase in optimization time is expected. The increase in optimization time varies from 17%–35%; however, this translates into only a 16–37 s increase in real time. Thus, the optimization time is still on the order of two minutes for all cases. Our clinical protocol for the baseline optimization uses a factor of five fewer iterations than used in this study. The optimizations were stable after a two‐fold increase in the number of iterations over the clinical baseline; so the times listed here should be considered an upper limit which can be reduced without any clinically noticeable loss in plan quality by a factor of 2.As noted in the Introduction, the effect of the radioactive decay half‐life can have an impact on the usability of seeds more than a week old. With activity reductions on the order of 40% after one week , it is nFirst and foremost, it is clear from From a clinical perspective, a challenging element of implementing the method presented in this work is calculating the real dose delivered to the patient due to the actual (rather than planned) implant. Since there is inherent seed placement error for PPI procedures, a post‐implant dose calculation is used to assess the potential efficacy of the implant. to obtain a one‐month, post‐implant CT of the patient, digitize the seeds, contour the organs and calculate the actual dose delivered. This may not be sufficient in hybrid cases since there are visually identical seeds with different activities — these must be identified. Thus it is necessary to create a one‐to‐one map between the planned seed positions and the actual positions.It has been standard practice via the recommendations of the Radiation Therapy Oncology Group (RTOG) for this procedure,19 t in PPI procedure is to obtain the post‐implant CT immediately after the implant procedure (rather than after one month). This would mitigate the difficulties in seed matching which stem from debulking and edema. Obtaining the post‐implant CT immediately after the procedure still would not account for the uncertainty inherent in placing seeds, but recent advances in post‐implant seed identification show that a one‐to‐one map can be generated. Brunet‐Benkhoucha et al. have shown that with only seven X‐ray images taken with a cone‐beam system subtending a 60° arc, seed detection rates of 96.7%, false negative rates of 3.3%, and false positive rates of 2.7% can be achieved. This is done by locating seeds to 0.4±0.4 mm in 3D space, and resulted in uncertainties in D90Prostate and V100Prostate of 1.5% and 0.3%, respectively.This procedure may be complicated by changes in the target volume and shape during the intervening one month between delivery and post‐implant imaging. However, a recent trend20 in 48 cm3 were chosen and reoptimized using the hybrid‐activity PPI optimization. These multi‐activity hybrid plans were equal in quality (as measured by the standard dosimetric indices) to plans with seeds of a single activity. Potential gains achievable by using different radionuclides have recently been explored in the literature; but, since the efficacy of incorporating the BED is still hotly debated in the community, we have focused on the multi‐activity hybrid plans. Despite the expanded search space, optimization times for these studies were still under two minutes on a modern day laptop and can be reduced to below one minute in a clinical setting. With the typical cost of a set of PPI seeds on the order of thousands of dollars, it is possible to reduce the cost of brachytherapy treatments by allowing for easier use of seeds left over from a previous patient or unused due to a cancelled or postponed treatment.An optimization algorithm that can generate hybrid brachytherapy plans was developed. Five previously‐treated PPI patients with a range of prostate volumes from 20 to


Furthermore, the sensor is able to operate at temperatures up to 110 °C, which is higher than the ones obtained for similar POF sensors in the literature.This paper presents a polymer optical fiber (POF)-based temperature sensor. The operation principle of the sensor is the variation in the POF mechanical properties with the temperature variation. Such mechanical property variation leads to a variation in the POF output power when a constant stress is applied to the fiber due to the stress-optical effect. The fiber mechanical properties are characterized through a dynamic mechanical analysis, and the output power variation with different temperatures is measured. The stress is applied to the fiber by means of a 180° curvature, and supports are positioned on the fiber to inhibit the variation in its curvature with the temperature variation. Results show that the sensor proposed has a sensitivity of 1.04 × 10 Temperature assessment plays an important role in industrial applications, such as automotives, air conditioning control, chemical processes, and food storage . FurtherIn order to overcome the limitations of conventional technologies for temperature measurement, different optical fiber temperature sensors have been proposed. The different approaches for temperature sensors include interferometric configurations, such as Fabry–Perot , Mach–ZeAlthough there are several optical fiber-based techniques for measuring temperature, the techniques related to FBGs, interferometers, and nonlinear effects generally imply more complex signal processing, implementation, and the cost of interrogation equipment can make these technologies unsuitable for low-cost applications . For a lHigher flexibility, fracture toughness, and strain limits enable the application of higher curvatures on polymer optical fibers (POFs) . The curIn this paper, a POF-based temperature sensor is proposed. The sensor explores the polymer response to different temperatures. Temperature increases lead to a decrease in Young’s modulus due to the viscoelastic nature of the polymer. Therefore, if the fiber is under stress due to a constant force, such stress on the fiber will change with the temperature variation. Since the stress on the fiber causes a variation in its refractive index due to the stress-optical effect , there wIn order to evaluate the Young’s modulus variation, dynamic mechanical analysis (DMA) on the fiber was performed. Furthermore, the relation of the fiber mechanical property variation with its optical response is modeled. After the validation of these effects, the POF-based temperature sensor is proposed and its temperature limits and repeatability are tested, and higher temperature limits compared to other intensity-variation-based sensors are obtained. In addition, the analytical model and DMA provide a different perspective on sensor design, which, from the authors’ best knowledge, has never been reported with respect to temperature sensors based on intensity variation. This paper is organized as follows. The optical fiber employed on the analysis presented in this paper is a multimode HFBR-EUS100Z POF  with a step-index profile. This fiber presents a core made of poly(methyl methacrylate) (PMMA) with a diameter of 980 µm. The cladding of this fiber is made of a fluorinated polymer that has a refractive index lower than that of PMMA. The cladding thickness is 20 µm. Furthermore, a polyethylene coating provides a total diameter of 2.2 mm for the POF.The described POF had its mechanical properties characterized through DMA, and the modeling of the power variation with the temperature is presented.The DMA comprises of the application of a predefined oscillatory load over a range of temperature and frequency. This is a widely applied method to determine the mechanical properties of a viscoelastic material such as the PMMA and polyethylene of the POF employed . Since tE′) and loss modulus (E″). The combination of the storage and loss moduli is the dynamic Young’s modulus (E*) of the polymer (see Equation (1)):Considering that a viscoelastic material presents a storage and a loss modulus on its dynamic response, the parameters obtained on a DMA include the storage modulus (δ)) defined in Equation (2). This is a ratio between the dissipated energy and the storage energy per cycle of applied load.The ratio between the storage and loss moduli is the loss factor (tan(δ) = 0).The Young’s modulus is divided into the loss and storage moduli due to the duality of a viscoelastic response, which is the combination of the elastic and viscous responses of the polymer. The loss modulus represents the energy loss due to the viscous effect, whereas the storage modulus represents the energy storage due to the elastic effect. Therefore, the loss modulus may be understood as the relation between the viscous and elastic components of the polymer response. For this reason, the loss factor of an elastic material is zero (tan(E*) Young’s modulus is defined by the phase shift between the input stress or strain and the viscoelastic response (δ). For this reason, another expression for the dynamic Young’s modulus calculation is presented in Equation (3):The relation between the static , which presents a temperature controller that can reach temperatures up to 400 °C with a resolution of 0.1 °C. In addition, the analyzer presents a resolution for the modulus estimation of 0.0001 Pa in a range of 10The presented test shows a high variation in the Young’s modulus with the temperature. For instance, when the temperature of 80 °C is reached, the variation starts to decrease until a temperature around 135 °C is achieved. On this temperature, the Young’s modulus cannot be measured, since its value becomes lower than the analyzer’s resolution. Furthermore, the exponential regression between the Young’s modulus and the temperature is obtained with a correlation coefficient of 0.9962. The obtained equation is presented in A laser (3 mW @650 nm) provided the light for the fiber under test and for a reference fiber, which was employed to compensate possible deviations of the light source power. The power was divided in a 50:50 coupling ratio through a light coupler IF-562 . The light power variation was acquired by two photodiodes IF-D91 , one for the reference fiber and the other for the fiber under test. The acquisition board USB-6008  with an 8-bit analog-to-digital converter and an acquisition frequency of 200 Hz acquired the photodiode response after it was converted to voltage through a transimpedance amplifier. In order to reduce the effect of the modal distribution in the light coupler, all the optical components presented, especially the light coupler, the reference, and the sensor fibers, were carefully positioned on a support to inhibit its geometry variation. Moreover, if there was any variation in the splitting ratio of the light coupler due to modal distribution changes, a power variation was detected in the reference fiber. This power variation could be further compensated by normalizing the sensor response with respect to the reference fiber.The fiber was bent on a 180° angle and positioned inside a climatic chamber 400/1ND , which had a closed loop control with a resolution of 0.1 °C and could reach a maximum temperature of 200 °C. A thermocouple was positioned close to the fiber curvature region to provide the reference temperature for analysis, which presented a temperature resolution of 0.1 °C and a temperature range of 220 °C. A lateral section was made on the fiber to create a sensitive zone, which could increase sensor sensitivity, since the stress on the fiber depends on the cross-sectional area . The incσ0) was calculated as Equation (4):R is the distance between the fixation points and the sensitive zone (60 mm), and x is the perpendicular distance between the bending part and the POF center, which is about 180 mm in the case presented in The curvature applied to the POF created a stress on the fiber. If pure bending stress was considered, the static stress (σ0) and the refractive index variation (∆n) is presented in Equation (5), where q11 is the stress-optical coefficient, which is about 10–11 Pa-1 for polymers [cn is the core refractive index, which is 1.49 for this optical fiber.The relation between the stress  is substituted in the Snell law to obtain the critical angle (see Equation (6)) and in the equation of the bending correction for the critical angle presented in Equation (7) [The effective refractive index is the core refractive index subtracted by the refractive index variation. This effective refractive index (tion (7) :(6)θc=coa is the core radius (0.49 mm), and cln is the cladding refractive index (1.417).In Equations (6) and (7), oP) [CS is the cross-sectional area of the core, oS is the cross-sectional area of removed material on the sensitive zone, and iP is the input power from the laser.Finally, Equations (6) and (7) are substituted in Equation (8) to obtain the POF curvature sensor output power (Po) :(8)PoPi=−1 is obtained. g). Since the Young’s modulus decreases with the increase in temperature, the stress on the fiber decreases, which leads to a reduction in the attenuation of the power when compared with the fiber in higher temperatures.The presented model is applied with the variation in the Young’s modulus with the temperature available on the DMA tests and a sensitivity of 1.13 °CThe application of a constant stress on the fiber increases its sensitivity to temperature variations. However, increasing temperature sensitivity by submitting the fiber to a higher stress can cause irreversible changes in the POF material. For a curvature sensor under stress with a temperature higher than rt, if the temperature decreases to rt, the POF response may not decrease to its original value. Therefore, in order to obtain a temperature sensor based on POF material features, it is necessary to apply stress to the fiber and position the fiber such that it does not change its curvature when its stiffness is reduced due to the temperature increase. Thus, the sensor will have less sensitivity with respect to the temperature variation when compared to the sensitivity that such a sensor might have if the maximum stress that the POF tolerates is applied. However, it will increase the sensor repeatability and enable the sensor to measure heating and cooling processes. For this reason, the curvature radius applied is higher than the ones presented in ,3.The temperature sensor developed has the same setup presented in −3 °C−1 and a linearity of 0.994. In addition, the root mean squared error (RMSE) of the proposed sensor was 1.48 °C. Comparing with similar sensors presented in the literature, the sensitivity was higher than the one presented in [−4 °C−1) and was close to the one presented in [−3 °C−1 for a curvature radius of 2 mm. The curvature radius of the sensor presented in this work was 10 mm. Furthermore, the sensor presented had higher linearity than the one discussed in [The sensor presented a sensitivity of 1.04 × 10The sensor repeatability was evaluated in five tests. Each test consisted of increasing the temperature to a predefined value and then decreasing it to the initial value. These tests were conducted to evaluate the capacity of the sensor to measure the heating and cooling processes and to set the operation limits of the sensor. The chosen temperature cycles were about 40 °C, 65 °C, 80 °C, 110 °C, and 140 °C. Although it was not assessed in POF response characterization or in the sensor calibration tests, a temperature of 140 °C was tested to evaluate the operation limit of the sensor.g was applied, and it can be seen that the sensor was not able to recover to its initial temperature (25 °C). This behavior is represented by an offset due to variations in the polymer properties when it was submitted to temperatures higher than Tg. In addition, this offset may also have occurred due to another phase transition that can occur at high temperatures, where there is a stretching of the polymer chains, which leads to irreversible changes of the sensor response. Finally, The only cycle that the temperature not returned to its original value was Cycle 5. This might have been due to the glass transition of the polymer, which led to an irreversible variation in its properties. Since 141 °C is higher than the POF glass transition temperature , the senThe response of each cycle shows repeatable behavior of the sensor, especially in the heating process. The only response that shows a slight deviation of this behavior is Cycle 2. Since the response of each sensor is shown with respect to time, the deviations in Cycle 2 are related to deviations in the heating process of the heater employed. Furthermore, the employed heater does not have a controllable cooling system. For this reason, the cooling process is limited to the natural convection between the heater and the room, which not only increase the time of cooling but also make this process less repeatable. Nevertheless, the results presented in This paper presents the characterization and development of a POF temperature sensor. The sensor is based on variation in the POF mechanical properties with temperature variation. If the fiber is under a constant stress, the variation in its mechanical properties leads to a variation in output power due to the stress-optical effect. The POF mechanical property variation is characterized in a DMA. An analytical model for the sensor behavior is proposed and simulated. Such a model is validated in temperature tests with the proposed sensor.Tests to obtain the sensor calibration curve and to evaluate its operation limits were conducted, and the sensor presented a sensitivity and linearity higher than those presented in the literature. Furthermore, the sensor is able to measure temperature until 110 °C, which is higher than the limit obtained in similar sensors presented in the literature. This temperature limit is due to the PMMA glass transition temperature and by applying POFs with materials that present higher glass transition temperatures, such as the step-index Topas/Zeonex  and CYTOFuture works include improvements on the hardware for the signal acquisition, which can enable the improvement of the sensor resolution. Furthermore, a heater with cooling capabilities and higher temperature precision will be applied to further evaluate the sensor with respect to its resolution and hysteresis.


Field width was varied around the 10 mm nominal size, between 9.7 and 11.1 mm, in steps of 0.2 mm. Mean difference between the step‐wedge analysis and the reference method was <0.01 mm±0.03 mm (1 SD). Laser (mis)alignment relative to a reference situation was detected with 0.3 mm precision (1SD). The step‐wedge profile was fitted to a PDD in water. The PDD ratio D20/D10, measured at depths of 20 cm and 10 cm, was used to check beam energy consistency. Beam energy variations were induced. Mean difference between step‐wedge and water PDD ratios was 0.2% ±0.3% (1SD). The completion procedure was able to reveal abutment mismatches with a mean error of ‐0.6 mm ±0.2 mm (1SD). The trending data over a period of three years showed a mean deviation of 0.4% ±0.1% (1 SD) in couch speed. The spread in field width was 0.15 mm (1 SD). The sagittal and transverse lasers showed a variation of 0.5 mm (1 SD). Beam energy varied 1.0% (1 SD). A mean abutment mismatch was found of −0.4 mm±0.2 mm (1 SD) between interrupted treatments. The on‐board MVCT detector, in combination with the step‐wedge phantom, is a suitable tool for a QA program for helical tomotherapy. The method allowed frequent monitoring of machine behavior for the past three years.The purpose of this study was to develop and evaluate filmless quality assurance (QA) tools for helical tomotherapy by using the signals from the on‐board megavoltage computed tomography (MVCT) detector and applying a dedicated step‐wedge phantom. The step‐wedge phantom is a 15 cm long step‐like aluminum block positioned on the couch. The phantom was moved through the slit beam and MVCT detector signals were analyzed. Two QA procedures were developed, with gantry fixed at 0°: 1) step‐wedge procedure: to check beam energy consistency, field width, laser alignment with respect to the virtual isocenter, couch movement, and couch velocity; and 2) completion procedure: to check the accuracy of a field abutment made by the tomotherapy system after a treatment interruption. The procedures were designed as constancy tool and were validated by measurement of deliberately induced variations and comparison with a reference method. Two Hi‐Art II machines were monitored over a period of three years using the step‐wedge procedures. The data acquisition takes 5 minutes. The analysis is fully automated and results are available directly after acquisition. Couch speed deviations up to 2% were induced. The mean absolute difference between expected and measured couch speed was 0.2% PACS number: 87.55.Qr It requires synchrony of gantry rotation, couch translation, linac pulsing, and the opening and closing of the leaves of the binary multileaf collimator (MLC). Furthermore, the system assumes constant output, and a constant size and shape of the longitudinal and transverse profiles. It differs in design and features compared to conventional linear accelerators, which implies that general quality assurance (QA) guidelines are not always applicable or sufficient. The aim of a QA program is to keep actual system properties within tolerances, close to a reference set. The Hi·Art system, which includes hardware and software for the delivery system and dose planning software, is commissioned in the factory to comply with a set of properties, common to all Hi·Art systems. This set is referred to as the Gold Standard beam dataset. The machine should be regularly checked for possible deviations from this dataset and adjusted, if necessary.Helical tomotherapy Hi·Art system  is an intensity‐modulating radiation technique for cancer therapy.,2 ItA QA program should aim at measuring the performance of isolated items in order to simplify error source tracking. The step‐wedge procedure is designed to do this. described tests for several types of binary MLCs. The study focused on MLC design and MLC alignment with other components like linac and collimator. Results from Sarkar's study support the use of the tomotherapy megavoltage CT (MVCT) detector for a number of QA tests.Sarkar et al.3 desc and Flynn et al. The average value of the output obtained during one measurement should still be within 2% while, in case of random angular dose rate variations, effects on delivered dose blur out in helical delivery and a less tight tolerance is allowed.The effect of variations in output and gantry speed on the delivered dose is discussed by Fenwick et al.4 and  described a QA program which includes megavoltage imaging quality, the accuracy of patient setup correction tools, spatial and temporal accuracy of the dynamic delivery properties, as well as more traditional beam output characteristics. This QA program uses film, water tank, ionization chambers, and the on‐board MVCT detector. It is shown that the transverse beam profile, measured with the on‐board MVCT detector, correlates well with ionization chamber measurements in a water tank. Balog and Soisson also presented a QA program for daily, monthly, and annual QA testing. This program includes procedures, specifications, and schedules that are currently implemented in the program recommended by TomoTherapy Inc.Balog et al.6 desc proposed a QA program that was based on the AAPM‐TG40 conventional linac QA schedule. The study provides a detailed overview of 23 relevant parameters to be divided into four categories: static beam dosimetry, system dynamics, system synchrony, and system geometry. Fenwick also proposed a time schedule, consisting of daily, monthly, every three months, and annual checks.Fenwick et al.8 propThis current study focused on the development of a filmless measurement tool for the relatively short term procedures (daily and monthly) proposed by Fenwick. Only procedures with a static gantry were considered. Some of these procedures are rather labor‐intensive and time‐consuming, especially the ones using film dosimetry. Our purpose was to implement a QA program for tomotherapy machines based on the overview given by Fenwick, but using the MVCT detector to collect attenuation signals and a step‐wedge phantom. With these tools, such a QA program would be extremely time‐efficient. The step‐wedge phantom was provided by TomoTherapy Inc., and was originally developed to monitor beam energy using an ionization chamber mounted on an in‐air scanner. The development of our QA procedures, including the algorithms to analyze the data, was performed independently and before development of the TQA application of TomoTherapy Inc. Most of the functionality discussed in this paper is currently available in the tomotherapy product. In extension to what TQA offers, this article describes and validates the completion procedure. It checks the abutment of an interrupted and resumed treatment. This paper is an independent confirmation and verification on the use of the tomotherapy MVCT detector for QA procedures. These procedures were designed as constancy measurement tools . The QA tools have been in use since August 2007. Trend information of machine behavior since then will be presented.Two QA procedures were developed: 1) step‐wedge: to check alignment of the lasers and the virtual isocenter, couch movement and velocity, beam energy consistency, and field width; and 2) completion: to check the accuracy of the abutment of treatment fields for a completion made by the tomotherapy system after a treatment interruption.≈0.7 mm and a length of≈25 mm, back projected at isocenter level. The source‐to‐axis distance (SAD) of a tomotherapy machine is 85 cm, the source‐to‐detector distance (SDD) is 142 cm in the center of the MVCT detector, and the radius of curvature of the MVCT is 110 cm. Because of this difference in MVCT curvature and SAD, ray lines emanating from the focus are bisecting the septa walls, except on the beam axis. Therefore, an increased signal is measured for the off‐axis MVCT detector cells caused by scattered photons, and a typical dip is seen in the profile center for the on‐axis cells , water temperature, water pressure and waterflow, air pressure, jaw settings, couch and gantry position. The data were extracted from the system through an FTP connection. All system signals were acquired at a frequency of 300 Hz and were down‐sampled to 30 Hz by averaging 10:1. This speeds up the process of data transport and analysis. The data files were analyzed and visualized with in‐house developed tools ). This study concentrates on the MVCT detector signals acquired with the step‐wedge phantom in the beam. The acquisition (beam on) time of the step‐wedge procedure is 300 sec.gr/cm3, according to the manufacturer (TomoTherapy Inc.). This corresponds to water equivalent steps of 52.5, 104.9, 157.4, 209.8 and 262.5 mm, respectively.The step‐wedge phantom is staircase‐like, and is attached to a rectangular support for alignment to the lasers with a notch to position the wedge on the couch such that it extends beyond the top of the couch see . If the The step‐wedge procedure is performed with a static gantry at 0°, a nominal field width of 1 cm, and with all leaves open, while couch and step wedge move 20 cm into the gantry. To be able to detect the transition from one step‐wedge level to the next, the longitudinal field size must be smaller than the longitudinal length of the CT detector channels . Therefore, this procedure is only suitable to be performed with the 1 cm field size. This is a minor limitation, because a drift in one of the parameters measured with the 1 cm field will also show up for the other field sizes, either because the parameter is independent of field size  or the parameter will be affected also for the other field sizes (energy).Only one set of collimator encoders is used for all field sizes. As a result, a drift in encoder value will have the biggest relative effect for the small 1 cm slit. So, if the 1 cm slit size is within tolerance, the bigger field sizes will be as well.All signals from the MVCT detector channels were corrected for fluctuations in the machine output as measured with the monitor chamber of the machine. The time profile analysis was performed using the signal from one centric CT channel  of the MTo enable an accurate analysis of the obtained signal, a model of the expected signal was made . The schfminsearch, MATLAB), the fit yields an accurate estimate of the optimized parameters, p1–p12 and s1–s7.A fit procedure was used to transform the schematic step‐wedge profile to the measured signal by minimizing the summed square of differences. Using an unconstrained nonlinear optimization technique ( MATLAB),10 theInitially, to test the robustness of the fitting procedure, the offset of the points was chosen randomly 10 times, resulting in p1 to lie in the interval of projection 1150–1250.R2>0.99). Therefore, the exact positions of the transition points between flat and sloped lines in the modeled profile are an adequate and accurate presentation of the edges in the measured time profile. Analysis of the profile  is reduced to analysis of a set of flat and sloped lines. Examples of measurements and analyses from the step‐wedge procedure are shown in The expected value of p1 is 1200. For each start position, the ‘least squares’ fit is performed and the best fit out of 10 is chosen. The result is a schematic step‐wedge profile which represents the measured profile. The resulting fit has a high quality . From this fit a percentage depth dose (PDD) is obtained  were obtained from the fitted schematic step‐wedge profile, and were used to check velocity consistency of the treatment couch and to measure the field width in longitudinal direction .±1%.Multiplying the numbers of projections between transitions  by the time per projection (30 msec), gives the time per level. The apparent length of each level is calculated by multiplying the time per level by the nominal couch speed. Comparison of the lengths obtained from this analysis with the physical lengths of the steps for all steps together and for the individual steps is a check on couch velocity, as well as uniformity of the couch velocity. Measured and nominal step lengths are plotted in The calculation of the field width of the irradiation beam is based on the number of projections between transition p2–p1, which corresponds to the first step of the step wedge. The field width is calculated by multiplying the time of projection p2–p1 by the couch speed. This value is referenced to an actual measurement of the field width using a reference method .Additional analysis from the time profile includes a check of the position of the transverse laser. Since the step wedge has been aligned to the lasers, which should be in the virtual isocenter, its exact longitudinal position was retrieved from the first transition in the profile: p1 in projections. This transition represents the moment the step wedge enters the irradiation beam. The projections are converted to position by multiplying with the ‘time per projection’ and the couch speed. This position is compared with a reference position.Transverse profiles were constructed from the signals of all 640 MVCT detector channels when the beam in longitudinal direction completely projects over the flat parts of the step wedge. The central part of both non‐normalized and normalized transverse profiles was described with so‐called modified Gaussian‐shaped curves:yfit the values of the dependent variable  calculated for all values xmeas of the independent variable (here CT channel). Fit parameters y0,a,x0,b,, and c represent offset, amplitude, center, width, and shape of the modified Gaussian model peak, respectively.with R2>0.99).A fit was performed (fminsearch) to transform this schematic step‐wedge profile to the measured profile. Fit‐parameters were used to determine the position of the center of the profiles, resulting in accurate, robust, and objective analyses. These positions could not be determined accurately enough directly from the measured profiles, due to noise in the measurements. The fit procedure assumes a symmetric profile, which is the case when the phantom is positioned close to the center of the slit beam. A small asymmetry can be seen in the transverse profile in x0 for the non‐normalized profile  is aligned perfectly, all three centers coincide. The position of the sagittal laser should coincide with the center of the irradiation beam . An example of an analysis result is shown in If the center position of the step wedge, calculated for all five steps of the step wedge, is not constant, the laser and/or the direction of the couch movement do not coincide with an axis perpendicular to the MVCT imaging plane. Such a mismatch does not affect the amount of step‐wedge material traversed by the beam, and thus will have no effect on the PDD.Similar to the method described above for the time profile, a PDD can be constructed from the (fitted) transverse profiles for the different steps of the step wedge . We can  we implemented a procedure to check the ‘completion’ . Such an interruption may happen during treatment of a patient. The tomotherapy system automatically generates a new procedure based on what remained of the treatment. An accurate abutment is necessary to prevent under‐ or overdosage in the abutment region.In addition to the tests proposed by Fenwick et al.8 we iTo perform a completion procedure, a step‐wedge procedure is interrupted during radiation at the point when the time profile of the step wedge shows a slope in between two flat parts. The new procedure is selected from the tomotherapy database and the remainder of the treatment can be delivered. The position of the step wedge on the couch top is not changed. To deliver the remaining part of the procedure, the couch is retracted first to the reference position such that the step wedge matches the transverse laser line again. In the analysis, the detector data files of both procedures were combined, and the translation correction needed for a perfect abutment of both step wedge time profiles is obtained in number of projections and converted to mm using ‘time per projection’ and couch speed. Since the completion procedure has a fixed gantry angle of 0° and all leaves are open, it only checks if the couch position is correct at the resumption of treatment delivery. This is not trivial because the couch has to move into the bore to the correct start position and has to travel at constant speed at the moment the beam is on and the leaves are opened. An example of the completion procedure data analysis is shown in The ability of the step‐wedge method to detect and quantify variations in beam energy, field width, couch speed, and laser position was measured and compared with a reference method.Beam energy was varied by varying the gun injector current (Injector I) between 3.1 and 5.0 Vdc. This was done on a nonclinical machine. Normal operating Injector I values lie between 3.5 and 4.5 Vdc. The ratio of the dose measured at depths of 20 and 10 cm (D20/D10) is a measure for beam energy. The measurement using step wedge and MVCT detector was compared with a measurement using solid water slabs and ionization chambers. The latter was considered the reference method. The solid water was placed on top of the couch and was kept at a constant position in the beam during these measurements. The measurement in solid water was done with two calibrated A1SL chambers at 10 and 20 cm depth, simultaneously. Two TANDEM electrometers from PTW  were used to collect charge. No correction was made for chamber shift. The reading was corrected for temperature and pressure and converted to dose with proper calibration factors. The ratio D20/D10 was varied 6% between 0.502 and 0.535.dmax=1.5cm. The surface of the solid water was at isocenter height. The position of the ionization chamber was checked with an MVCT and corrected if necessary. Field size was defined at FWHM of the beam profile and scaled to isocenter. The ionization chamber method was considered the reference method. Note that both methods assume a constant and known couch speed. The couch was driven through the radiation field with a constant speed of 1 mm/sec, which was the same speed as used for the step‐wedge procedure. The chamber electrometer sample rate was 0.2 sec. The gantry was kept stationary at 0°. The field width of the nominal 10 mm slit was varied between 9.7 mm and 11.1 mm in steps of 0.2 mm by varying the encoder values of the back jaw and front jaw of the tomotherapy collimator system.Field width and couch speed were varied by changing corresponding parameters in the tomotherapy procedure application. The field width measured with the step‐wedge procedure was compared with a measurement performed with an ionization chamber in solid water. The ionization chamber was oriented with its long axis perpendicular to the couch movement. It is positioned in the center of the beam and at depth −2% and +2% from the default value of 1 mm/sec.It was checked if the step‐wedge procedure was capable of detecting different velocities of the couch table top, induced by changing the appropriate parameter in the tomotherapy procedure application. The different speeds inputted in the tomotherapy application served as the speeds ‘that should be measured’. The couch speed was varied in steps of 0.5% between −3mm and +3mm, in steps of 1 mm. For the lateral laser, the in‐out position of the step wedge was varied between −3mm and +3mm.A deviation of the sagittal and longitudinal laser to the calibrated position was simulated by varying lateral and longitudinal position of the step wedge in the beam. The couch drive mechanism was used to achieve this. The accuracy of couch positioning was checked with a micrometer dial gauge and was accurate within 0.1 mm. For the sagittal laser, the left‐right position of the step wedge was varied between −1.0, −0.5, 0, 0.5, and 1.0 mm) of the couch when the system was set up for the remaining part of the treatment.To check the accuracy of the completion method, an experiment was set up to simultaneously measure the abutment with film (EDR2) and with step wedge. A film was placed on the couch and the step wedge was placed on top of the film. The film was processed in an AGFA CP1000 film processor. Mismatches were introduced by small longitudinal translations . The mean optical density in the abutment region was 0.8. A line profile was taken perpendicular to the abutment and was analyzed in a spreadsheet.With film slightly lower peak and dip maxima were found: 6% for the 1 mm gap/overlap and 3% for the 0.5 mm gap/overlap. Although it was not trivial to relate the height of the peak or dip with a mismatch in mm because of the presence of film processing artifacts, the film measurement was able to reveal a mismatch of 0.5 mm.−0.6 mm, with a variation of 0.2 mm (1 SD). Despite of this gap, the spread was small and the step‐wedge results closely followed the induced mismatches.The simultaneously‐performed completion procedure with the step wedge showed a gap of The second column in  Measurement of the coronal laser and output are not part of this step‐wedge procedure and will have to be measured separately.A quality assurance (QA) program for helical tomotherapy differs from a QA program for conventional linear accelerators because of its unique design . We have developed two QA procedures which focus on the delivery system, using the on‐board MVCT detector, the step‐wedge phantom and system parameters recorded during radiation. The approach is time efficient. It allows the measurement of multiple items in one procedure, with a fully‐automated analysis. The whole procedure takes about 15 min, whereas measurement of the same QA items with conventional methods (ionization chamber and film) would take about 90 min. The QA items involved should be measured on a weekly and monthly basis as described by Fenwick et al.8 Meas In accordance with TG142, the tolerance for beam quality variations is 1%. The step‐wedge procedure allows an easy beam energy consistency measurement with an accuracy within 0.5% . It should be noted that this data comprises the history of a number of target changes. Due to target wear, the energy drifts slowly to lower values, which subsequently can be corrected by tuning the Injector I. As can be seen from the trending data in  Typically, the dose delivered will change approximately ±2% if the nominal 10 mm field width changes by 0.2 mm. The step‐wedge procedure allows fast and accurate measurement of changes in field width. Trend data for a period of three years shows a variation in field width of 0.15 mm (1 SD).The constancy of the longitudinal profile shape and size is particularly important for helical tomotherapy. The dose to the patient is the integration of the longitudinal profile with couch motion.13 TypThe position of the transverse laser (in–out position) and the sagittal laser (left–right position) can be accurately measured using the step‐wedge method. It should be noted that an incorrect position of the lasers will not result in an incorrect patient treatment if online 3D setup verification and correction is performed using the build in MVCT detector.Theoretically, also the position of the coronal laser (the height of the couch) can be checked with the step‐wedge procedure by analyzing the width of the transverse profiles. However, this measurement was not sensitive enough to deviations in laser position (> 1 mm). To measure the position of the coronal laser, it is better to operate the step‐wedge procedure with a gantry angle of 90° or 270° or in helical mode.The tomotherapy control system allows online monitoring of machine parameters with a sample rate of 300 Hz. An interrupted treatment, therefore, can be continued with high abutment accuracy. This is especially important because of the sequential nature of the tomotherapy dose delivery. The completion procedure presented in this study is time‐efficient, and is able to detect abutment errors of 0.5 mm. This will reveal under‐ or overdosage of 3% at the abutment region (based on film measurements). An advantage of the step‐wedge analysis is the unambiguous presentation of the mismatch in mm. The completion procedure described here is performed with the gantry fixed at zero degrees. The clinical value in performing such a limited test is that this procedure tests the ability of the system to start the remainder of the treatment at the correct couch position and with the correct synchronization of MLC and couch. These parameters are also crucial for a correct treatment.In clinical practice, the accuracy of the abutment will also be influenced by correct positioning of the patient on the couch. To verify this, 3D patient setup verification should be performed with the on‐board megavoltage CT detector just before the delivery of the remainder of the treatment.In the Hi•Art II system, couch movement is not synchronized with gantry movement and MLC dynamics. Careful and frequent control of couch speed and couch speed uniformity is therefore important. Trending data shows the variation in couch speed during three years is limited  and within tolerances (1%).While synchronization of couch with gantry and couch with MLC is not controlled by the system, it is important to have a tool to measure and analyze the synchronicity of these items. To achieve this, the step wedge should be adapted for use in helical mode. This will also allow one to analyze the completion procedure in a more comprehensive way — not only the abutment of fields based on correct couch positioning, but also based on correct gantry position and leaf motion.The step‐wedge method showed reproducible results in repeated analysis on the same dataset. The variations were small relative to the variations seen in the trend data . This imThis study shows the suitability of a step‐wedge phantom and the on‐board MVCT detector of the tomotherapy Hi•Art for QA purposes. A comprehensive set of QA items can be measured accurately in a quick, simple, and automated way. The new method allowed frequent monitoring of machine behavior for the past three years.TomoTherapy Inc. is acknowledged for providing the step‐wedge phantom.


The death of a simulated patient is controversial. Some educators feel that having a manikin die is prejudicial to learning; others feel it is a way of better preparing students for these situations. Perceived self-efficacy (PSE) reflects a person’s perception of their ability to carry out a task. A high PSE is necessary to manage a task efficiently. In this study, we measured the impact of the death of a simulated patient on medical students’ perceived self-efficacy concerning their ability to cope with a situation of cardiac arrest.n = 27), pre-graduate medical students were warned of the possible death of the manikin; group 2 students were not warned (n = 29). The students’ PSE was measured at the end of the simulated situation and after the debriefing.We carried out a single-centre, observational, prospective study. In group 1 (p = 0.41). It had significantly progressed at the end of the debriefing . No significant difference was noted between the 2 groups (p = 0.382).The PSE of the two groups was similar before the debriefing  contains supplementary material, which is available to authorized users. Simulation is an educational method which is used to expose learners to situations which are close to reality, in all its diversity, in an environment which is safe for both the patient and the health professionals , 2. The the subjects’ personal experiencesobserving colleagues onto whom the subject projects himselfverbal persuasion; i.e. a judgment, whether positive or not, held by others concerning the tasks carried out by the person in questionpsychological and emotional states.At the same time, work has revealed that medical and nursing students are not satisfied with the training provided in how to handle death and announcing bad news . They doSituations experienced in simulation are likely to influence the PSE of students attending simulation sequences, with respect to the different determinants.The expected death of the simulator, when trainers and learners know from the start that the manikin will lose its vital signs at a given moment. This type of scenario is often used to teach end of life situations with palliative care or oncology teams.Death resulting from an inappropriate action or failure to act by the student.Unexpected death, in which the trainer knows that the simulator will show an absence of vital signs. The learner will discover it during the development of the situation. Death then occurs as a result of an expected acute complication from an existing pathology and not as a consequence of an inappropriate action by the student.In terms of scenarios of critical clinical situations, the literature describes three types of situations which lead to an absence of vital signs on the simulator :The expeThe latter is more commonly encountered in emergency medicine, where its unexpected nature often leaves caregivers with the impression that they are helpless and inadequately trained to cope with this situation in both clinical practice, or even during a simulation session with a teacher and colleagues . A certaAfter obtaining the approval of the Strasbourg faculty of medicine ethics committee, we carried out an observational, single-centre, prospective study. Medical students were subjected to a simulation session during which the manikin, simulating an acute pathology , was programed to rapidly show an absence of vital signs. The absence of vital signs in the manikin corresponded to the presence of apnea combined with an absence of the carotid pulse. The students’ PSE relative to taking charge of situations of sudden death was assessed at the end of the simulated sequence and after the debriefing.A total of 56 students consented to, and were included in the study. There were 34 women and 22 men. Eight simulation sessions were organized with groups of seven students per session. All subjects were undergraduate students, mostly in their final year of medical training. Students were randomized into two groups: the first group (Group 1) attended a specific prebriefing on the possible death of the manikin; the second group (Group 2) had a conventional prebriefing with no mention of the possible evolution of the simulator towards an absence of vital signs.The patient was simulated by a high-fidelity manikin (Gaumard®) placed on a stretcher in an emergency room setting. The simulated session followed the usual three phases of this type of activity Table , with anThe eight simulation sessions were presented by four experienced simulation instructors trained in debriefing. They were all given a detailed description of how the simulation session would proceed, in order to guarantee homogeneity from one group to the other. In each group, the prebriefing and debriefing were both led by the same instructor.We measured the students’ PSE relative to taking charge of situations of sudden death, using the Motivated Strategies for Learning Questionnaire (MSLQ) developed by Pintrich et al. , which hThe questionnaire was given to each participant before the debriefing (pre-test) and at the end of the training session (post-test).p-value was less than 0.05. The Likert score was calculated by adding up the Likert items. The averages of the Likert scales for both groups were compared using Student’s t test. A difference of less than 5% (p < 0.05) was considered to be significant. The data are available as a supplemental file . Students replied that they were confronted by the sudden death of a patient several times a year, with no difference between the two groups (p = 0.557). Group 1 had received more training than Group 2 in how to announce bad news (p = 0.016), whereas the level of training in managing a patient death was identical between the two groups (p = 0.232). The level of clinical experience between the two groups was similar (p = 0.557), with most of them being in their 6th year of medical training.The distribution of men and women was identical in both groups (p = 0.41). The PSE also progressed significantly after the session (p < 0.001)  Table .Table 3Rp = 0.382).Therefore, training appeared to have a positive effect on the evolution of the PSE. This progression occurred in the same proportions in both groups; there was therefore no significant difference between PSE levels, whether or not the group had been given the specific prebriefing (Our study revealed that undergraduate medical students near the end of their training, faced with a simulation of sudden patient death, improved their PSE relative to management of this type of situation. This study corroborates the position taken by Gordon et al. and by Gettman et al., according to whom training by simulation scenarios including the unexpected death of the manikin provides an opportunity for students to develop or improve their skills, with PSE constituting a necessary part of the efficient implementation of these skills , 22.These results also reveal the potential benefits of exposing students to simulator death, as has already been reported by several other researchers –26. To gThe fact that carrying out a prebriefing announcing the possible death of the manikin is not necessary is an interesting result. Not having to make such an announcement allows to maintain a form of authenticity to the simulation. The fact of drawing participants’ attention to this possible outcome may indeed lead them to increase their vigilance over the occurrence of this event, even though they would not have done so in a real situation. This could therefore compromise their clinical reasoning by pointing it to the signs to pay attention to during the sequence, even though recognizing the nature of the problem in a simulated or real clinical environment by picking up the relevant information is central to the medical expertise, particularly in the emergency medicine practice , 29. ThiOur study has some limitations. Apart from the single-centre aspect, the sample size was limited. In addition, the increase in PSE was not evaluated at any other point following the training in order to evaluate whether the learning from the session endured. Moreover, the questionnaire was translated from English into French, which may have impaired its psychometric qualities, although we have been particularly careful during this translation phase. This work should be continued in a multi-centre context, with a larger and more diversified population of students. Finally, we cannot exclude that filling out a questionnaire about PSE directly influences the respondents’ PSE and impacts on the students processing of the simulation experience.The originality of our study lies in the fact that we got interested in a particular aspect of the development and implementation of professional skills: perceived self-efficacy. We therefore wanted to provide original and unpublished light on whether or not exposing students to the death of a simulation manikin is useful or not.Our work tends to corroborate the assertion made by some researchers of the benefits of exposing medical students to unexpected death when being trained in a simulated environment. Specifically, we showed that the absence of a prebriefing specifically surrounding simulated death has no negative effect on the PSE of undergraduate medical students relative to taking charge of situations of sudden death.


It relies heavily on “otcetera”—a set of C++ tools to perform most of the steps of the pipeline. All of the components are free software and are available on GitHub.We present a new supertree method that enables rapid estimation of a summary tree on the scale of millions of leaves. This supertree method summarizes a collection of input phylogenies and an input taxonomy. We introduce formal goals and criteria for such a supertree to satisfy in order to transparently and justifiably represent the input trees. In addition to producing a supertree, our method computes annotations that describe which grouping in the input trees support and conflict with each group in the supertree. We compare our supertree construction method to a previously published supertree construction method by assessing their performance on input trees used to construct the Open Tree of Life version 4, and find that our method increases the number of displayed input splits from 35,518 to 39,639 and decreases the number of conflicting input splits from 2,760 to 1,357. The new supertree method also improves on the previous supertree construction method in that it produces no unsupported branches and avoids unnecessary polytomies. This pipeline is currently used by the Open Tree of Life project to produce all of the versions of project’s “synthetic tree” starting at version 5. This software pipeline is called “ One priTranslating the goals of the Open Tree of Life’s summary tree into an explicit set of criteria is not trivial. The summary supertree should represent the phylogenetic information from source trees in a transparent and justifiable fashion. We would like to allow users to correct errors in the supertree by improving the input information rather than requiring modification to the supertree algorithm. The pipeline was designed to create a tree which: 1.displays no unsupported groups, 2.defers to groupings from higher ranked trees in the case of conflict, 3.contains no unnecessary polytomies, and 4.displays as many groupings from input trees as possible.These goals are described more fully below. In order to accomplish transparency and justification, our pipeline also produces annotations files with information about conflict and support.We require that each edge in the supertree be supported by at least one input tree edge. In addition to aiding interpretability, this requirement keeps the supertree from arbitrarily representing information that comes from none of the input trees. Of course, in a supertree analysis, the full tree will imply some relationships for subsets of the taxa that are not found in any input tree. So, the meaning of “supported by” needs some clarification.Notation, terminology, and the definition of “supported by” Cutting any edge j of a rooted tree induces a bi-partition S(j) of the leaf taxa into two connected groups S1(j) and S2(j). Such a bi-partition is called a rooted split and written S(j) = S1(j)|•S2(j). Here S1(j) is called the “cluster” of j, and contains tip taxa on the side of edge j that does not contain the root. S2(j) contains the tip taxa on the side of edge j that contains the root. We refer to S(j) as a rooted split because the right side of the split implicitly contains the root node, as indicated by the •.A = A1|•A2 and B = B1|•B2, we say that A displays B if B1⊆A1 and B2⊆A2. We also say that A and B conflict if none of A1∩B1, A1∩B2, and B2∩A1 are empty. Note that the intersection of the right sides of the splits is considered to be non-empty even if A2∩B2 is empty, because the right sides implicitly contain the root. If A and B do not conflict, then we say that they are compatible.1A and B if and only if A and B are compatible.It is possible to construct a rooted tree that displays both If A displays B, then A and B must be compatible, since A1∩B2 and B1∩A2 must be empty. We adopt the shorthand of referring to an edge k when we mean the split S(k). So, when we say that edge j conflicts with edge k, we mean that S(j) conflicts with S(k).For any two rooted splits Ti denote the ith input tree. We say that 𝕊 displays an edge j of Ti if any edge of 𝕊 displays j. We say that 𝕊 conflicts with a edge j of Ti if any edge of 𝕊 conflicts with j; otherwise 𝕊 is compatible with j. We say that edge k of 𝕊 is supported by edge j of Ti if 𝕊 displays j, but 𝕊 would not display j if we contracted edge k. We also adopt the shorthand of referring to edges by their tipward endpoint. By combining this with the above shorthand, we could say that a node j conflicts or does not conflict with another node k.Let 𝕊 denote a supertree, and Ti is i) denotes the summary tree induced by tip nodes that are mapped to taxa in The set of taxa that are mapped to the tips of the tree Note that stating that a node in the summary tree is supported by an input does not imply that every descendant of that node must be present in the input nor that every taxon that is not a descendant must be excluded in order to display the node. Consider the problem shown in One of our aims in supertree construction is to minimize the amount of information in the supertree that does not come from input trees. We permit information that comes from combinations of input trees, but not any single input tree. However, we seek to exclude information that comes from none of the input trees. This motivates the criterion of not having any unsupported edges, since these edges could be removed without decreasing the support from any input tree.NP-hard via a reduction from Max-Clique , and it allows simpler and cleaner algorithms.c conflicts with a higher-ranked edge b, then c may still be included in the supertree. This can occur when the higher ranked edge b conflicts with a yet-higher ranked edge a, and thus b is not included. In that case, it will be possible for c to be represented in the summary tree. Thus, the fact that the summary tree displays an input edge does not imply that none of the higher ranked input trees conflict with that edge.Note that if some edge In order to produce a comprehensive supertree, we also require a rooted taxonomy tree in addition to the ranked list of rooted input trees. Unlike other input trees, the taxonomy tree is required to contain all taxa, and thus has the maximal leaf set. We make the taxonomy tree the lowest ranked tree. In our current formulation, the taxonomy tree is also unique in that the taxonomy is the only source of taxonomic names. Each node in the taxonomy tree corresponds to a named group. Taxonomic groups may have the same name, but each node in the taxonomy tree is identified by a unique number (its OTT ID). Taxonomic groups are identified in the summary supertree by finding a branch (or “node”) that has exactly the same include|exclude relationship. The taxonomy supertree can meaningfully possess degree-two nodes. Although these nodes can be removed without affecting the relationships of the leaves, they do represent nested taxonomic groups that contain exactly one subgroup. The taxonomy is also used to determine which tips are terminal taxa.not included, we can point to a reason for non-inclusion by showing that the input edge conflicts with some edge of the summary tree. Note, that the requirement to not display unsupported groups leads to some “necessary” polytomies. For example any resolution of the polytomy shown in The supertree should be as resolved as possible—in other words, it should have no unnecessary polytomies. Thus, for each input edge that is We also seek to construct a supertree that represents as many input tree nodes as possible. Since non-included input tree nodes must conflict with the supertree (or they would have been added), this criterion is the same as minimizing the number of input nodes that conflict with the supertree.Many researchers construct supertrees for the purpose of inferring the true tree from the input trees . Such apsummarize and represent the input trees, even when they conflict with each other. The annotations file constructed by our pipeline is essential for this task because it includes information about conflict with our supertree as well as support for it.Our supertree method operates in a different paradigm. Phylogeny estimation is an explicit non-goal of our supertree algorithm. We do not claim that the supertree is more accurate than the input trees it summarizes, and so conflict resolution is not a primary aim of the method. Most of our input trees have non-overlapping taxon sets, and so conflict resolution is not the primary problem. Instead, we seek to merge and aggregate phylogenetic information on the largest possible scale. Therefore, the primary aim of our supertree method is to Our method must resolve conflicts in order to construct a single supertree. However, the rank information used to resolve conflicts is an input to the method, not an output from the method. We thus perform curation-based conflict resolution, not inference-based conflict resolution.unique optimum. The pipeline takes a greedy approach to producing a summary tree by attempting to add groupings from the trees in order of the trees ranking. This can be viewed as a greedy solution to the problem of finding the tree with the maximum sum of displayed groups’ weighted scores criterion  is one of the most widely used supertree approaches . MRP attAnother commonly used method is MinCut  or Modifphylo_input subdirectory) and store copies of these files (in the phylo_snapshot subdirectory) to make it easier to replicate the operation (because the collection of trees and the tree files change due to curation).Propinquity was designed to function as a part of the Open Tree of Life software architecture, so the first few steps of the pipeline involve transforming artifacts from that project into a set of rooted trees and a phylogenetic taxonomy. The phylesystem API  of Open config.opentree.synth file in the propinquity repository). For the purpose of the rest of the pipeline, an OTT ID that has been pruned from the taxonomy will be treated in the same way as invalid OTT ID. The output of this step is stored in propinquity’s cleaned_ott subdirectory; this operation only needs to be performed when the OTT or the pruning flags change.OTT is a hierarchy of taxonomic names that implies a phylogenetic taxonomy. An OTT ID has a position in the hierarchy, a taxonomic name, and set of references to the same name in different taxonomies. In addition, the ID may also be associated with a set of flags that can indicate that the taxon may be questionable. These flags can either encode information taken from an input taxonomy  or can arise because of some form of conflict during taxonomy construction . Propinquity prunes the OTT down to a more reliable taxonomy by pruning off parts of the tree that are flagged with suspicious flags. The set of flags that lead to a subtree of the taxonomy being pruned is under the control of the user , c) with , c) instead of the subtree A, B), c).Many input trees have tips that are not terminal taxa, but higher-order taxonomic groups. It is not clear how to interpret a tip in a phylogenetic estimate that is labeled with the name of a higher taxon. Several scenarios can lead to these cases: the data for the tip could have been created by merging a chimeric set of character scores from constituent taxa; the species sampled may not have been identified to the lowest taxonomic rank; or the researcher may simply have used a higher taxonomic name because he/she assumed that the taxon is monophyletic and the higher level name would be more recognizable. Rather than allowing the ambiguity about interpretation of the higher-taxon mapped tips to propagate throughout the entire pipeline, we transform the input trees by replacing higher taxa at tips with a set of terminal-taxon exemplars for each taxon. One approach would be to simply determine all descendant terminal taxa and attach them as children of the problematic tip. However, this would create a clade rather than a tip; subsequent steps in the supertree would interpret the clade as a claim of monophyly for the taxon. The input tree may not have tested monophyly of the clade, so this interpretation is unwarranted. We avoid it by attaching exemplar taxa as child nodes of the higher taxonomic tip but then collapsing the edge between the former tip node and its parent. Thus, if otc-nonterminals-to-exemplars) reads the taxonomy and all of the “cleaned” phylogenetic estimates from the previous step. Reading all of the inputs is necessary to assure that each higher taxon is replaced with the same set of exemplars regardless of which tree the higher taxon occurs in, and that the exemplars for a higher taxon is the union on the set of descendant terminal taxa that have been sampled in a phylogenetic input. For example, the trees in If a taxon is only present in the taxonomy (not in any of the input trees), then it can be pruned from the taxonomy for the construction of the supertree and then grafted back on to the summary tree later. Performing this pruning reduces the size of the supertree problem, reducing the running time of the pipeline. Similarly, when we expand a higher taxon in the exemplification step, we can omit members of the taxon if they do not occur in any of the phylogenetic inputs. If there are no members of the higher taxon sampled in any other input tree, then we arbitrarily choose one terminal taxon to represent the higher taxon. During the exemplification step, a tool from otcetera  criterion. This is accomplished in four steps: (1) dividing the full problem into subproblems based on uncontested taxa; (2) constructing a summary solution for each subproblem by greedily creating a maximally-sized list of groupings that can all be displayed simultaneously; (3) grafting the subproblem solutions into a single supertree; and (4) grafting (or “unpruning”) the taxonomy-only taxa onto the solution to produce a complete summary tree.Ti contests taxon x in the pruned taxonomy, if x is not monophyletic in any resolution of tree Ti. Thus, polytomies in an input tree are treated as soft polytomies, and a taxon is not contested merely because it is not displayed by an input tree.For the sake of efficiency, propinquity uses a divide-and-conquer approach to construct the supertree. Subproblems are identified by searching through the taxonomy tree to find any taxa that are not contested by any single input tree. Here we say that input tree otc-uncontested-decompose tool in otcetera; see This operation is performed by the B shown in the taxonomy panel B1 and B2 in the input phylogenetic trees would be sliced during the decomposition, and relabeled to refer to taxon B. This taxonomically-informed interpretation of the inputs views the two phylogenetic inputs as in conflict; so the solution returned by propinquity would defer to the higher ranked tree. The tree shown in B, but neither contests taxon B when the trees are considered in isolation.Note that decomposition into uncontested groups does not necessarily allow us to find the tree that maximizes the MSDGWS score. For example, see otc-uncontested-decompose can worsen the final score of the summary tree, we use this approach in propinquity because it makes the construction of the tree faster and it is easy for users to correct issues caused by incorrect taxa being constrained to be monophyletic. By adding a tree (even a low-ranked tree) that contests a taxon to the corpus of input trees, then the next synthetic tree will no longer consider the taxon to be uncontested. Thus the procedure encourages curation of more phylogenetic inputs as a means of improving the summary tree.Despite the fact that the use of When solving sub-problems, we sequentially incorporate splits from trees in order of ranking, retaining splits that are compatible with the current set of splits . The ordO(N2 + N2E + NL) where N is the number of splits passed in, E is the average size of the exclude group, and L is the total number of leaves. This simplifies to O(N2) if all splits are triplets. In this approach splits are either entirely retained or entirely discarded—consistent rooted triplets from conflicting splits are not retained. However, when unpruning taxonomy-only taxa (see below), we make an attempt to break ties in a way that preserves some partial information from conflicting splits by attaching taxa from conflicting splits at their common ancestor. The BUILD algorithm as originally stated by grafted_solution/grafted_solution.tre by propinquity); see To produce a tree that spans all of the taxa sampled in the exemplified set of input trees, we graft the subproblem solutions into a single tree . The deterministic choice of designators also makes the labeling insensitive to branch rotation of the grafted solution tree.In order to annotate each node in the summary supertree, it is first necessary that each node have a unique identifier. Nodes whose include group correspond exactly to the include group of a node in the taxonomy are given the same identifier as the corresponding taxonomy node. These identifiers are of the form otc-annotate-synth tool from otcetera to create an annotations file describing the pipeline used and the connections between phylogenetic information in the inputs and the summary. The JSON file produced by otc-annotate-synth encodes a “nodes” property that holds a mapping between a node name for the summary tree (using the naming convention described in the previous section) and a node provenance object that categorizes the relationship between the node and the inputs. The node provenance object for node x uses several properties to categorize the relationship between the node and the inputs; each property in the node provenance object maps to a structure storing the tree ID and node IDs for the input tree nodes.To reveal the connections between the groupings found in the a summary supertree and the input trees, propinquity uses a few Python scripts and the j in each input tree i and the summary tree node x. Because the vast majority of nodes in the input studies will be compatible but not directly relevant to node x we do not list all of the compatible groupings. If node x is not included in the induced tree 𝕊(i), then none of the nodes of tree i will be referred to in the annotations for node x. Even if x is included in 𝕊(i), many of the nodes of Ti will be compatible with x while being relevant to other parts of the summary tree. The only input nodes listed for node x are with rooted taxon bipartitions which conflict with, are displayed by, or are resolved by the the rooted taxonomic bipartition associated with node x. All input nodes that cannot be displayed by any supertree that contains x are stored in a “conflicts_with” property of the node provenance object. If node j of Ti is displayed by the summary tree and x is part of the path of 𝕊(i) that displays the split between descendants of j and other taxa, then a reference to the node j will be in the node provenance object. The exact categorization of this annotation will depend on the configuration of node x on the induced tree 𝕊(i):Conceptually, this annotation operation is equivalent to considering every node  •x is on a terminal path in the induced tree then node j will be listed in the “terminal” property;if  •x is along an internal path that contains some nodes with out-degree equal to 1, then node j will be listed in the “partial_path_of” property; andif  •x is along an internal path without any node of out-degree 1, then node j will be listed in the “supported_by” property, because node j supports the existence of grouping x in the sense that collapsing the edge that separates x from its parent would cause the summary tree to no longer display node j.if Ti does not display x from 𝕊(i), but there exists an unresolved node j in Ti which could be resolved such that the tree would then display x, then a reference to node j will be listed in the “resolves” property of node x.These three relationships are illustrated in j in Ti could further resolve a polytomy x in the summary tree; such a case would be annotated using the “resolved_by” property of x. However, because of our goal of excluding unnecessary polytomies, none of the nodes in propinquity’s summary tree will use this annotation when they are annotated with the set of input trees.The otcetera annotation tool can also detect cases in which including information from node An optional step in the propinquity pipeline (triggered by the executing the “make html” target) can compose an “index.html” file for each directory created during the pipeline to explain the artifacts held in that directory and report summary statistics about the summarization run.We seek to assess the performance of our new supertree method by comparing it to the supertree method of The flag-cleaned version of OTT used in the construction of both supertrees contained 2,424,255 leaves. The OTLv4 supertree was constructed from 482 phylogenetic inputs, containing a total of 45,385 leaves, of which 41,029 were unique. After flag-cleaning and exemplification by propinquity, these trees contained 40,323 unique tips. We used the same cleaning flags to trim the taxonomy and input trees when constructing OTLv4′, so OTLv4 and OTLv4′ contain the same number of leaves.In the OTLv4′ summary tree, the decomposition procedure produced 5,406 subproblems, but only 1,422 of these were non-trivial to solve. If a subproblem contains only two tips it is trivial; 2,362 subproblems were trivial in this way. Similarly, if a subproblem contains only 2 trees it is trivial to solve because the solution will simply be all of the groupings from the first tree combined with all of the groupings from the second tree that are compatible with the first tree; 3,052 subproblems were trivial in this way. The subproblem with the largest number of tips contained 946 tips. The largest subproblem, in terms of the number of input trees (including the taxonomic tree) that were relevant, had 16 trees. Without decomposition, the supertree problem would have had 482 input trees and 41,226 leaves.supported_by, partial_path_of, terminal, conflicts_with, or resolved_by. For example, the numbers in the conflicts_with column indicate the number of input splits j with at least one summary edge x such that the relation “xconflicts_withj” holds. The total number of non-terminal input phylogeny splits considered was 40,996.We performed an annotation of both the OTLv4 tree and the OTLv4′ tree as described in section ‘Annotation’ to assess the ability of our new supertree method to represent splits from input phylogenies. supported_by + partial_path_of) increased from 35,518 (for OTLv4) to 39,639 (for OTLv4′); an 11% increase. When examining which splits are displayed, we find that the OTLv4′ tree displays 4,920 input splits that are not displayed by the OTLv4 tree, whereas the OTLv4 tree displays only 799 input splits that are not displayed by the OTLv4′ tree. The number of input splits that conflict with the summary (conflicts_with) dropped from 2,760 to 1,357, a decrease of 1,403, or 51%. In accordance with the goal of not containing unnecessary polytomies, the number of input splits that do not conflict with the summary tree, but are not incorporated (resolved_by) dropped from 2,718 to 0. We also find that the number of polytomies in input phylogenies that are resolved by the summary tree increases from 473 for OTLv4 to 515 for OTLv4′.The number of displayed input splits  to propinquity  has increased the pace of synthesis tree releases from the Open Tree of Life project. This is partly because the newly available annotations feature has made it possible to identify which input trees are responsible for taxa being included or excluded from the summary tree. Additionally, the new propinquity software pipeline has decreased the computational time required to construct a supertree from several hours to about 8 min (after some preprocessing steps which only have to be performed when the input taxonomy changes). The amount of RAM required during tree construction has also decreased substantially.10.7717/peerj.3058/supp-1Appendix S1Click here for additional data file.10.7717/peerj.3058/supp-2Appendix S2Click here for additional data file.