In Experiments 2 and 3, no n − 2 repetition cost was obtained following trials truncated after cue presentation. To ensure some task preparation on cue-only trials, Experiment 4 used a double-registration procedure where participants responded to the task cue and the target on each trial. In contrast to Experiments 2 and 3, a small n − 2 repetition cost followed trials truncated after cue responses, affecting cue responses on the current trial. In addition, the n − 2 repetition cost was increased at cue responses and became evident at target responses when the preceding trial also involved a target response. These results imply that backward inhibition might be generated by processes occurring up to and including a cue response, affecting subsequent cue responses, as well as during task performance itself, affecting subsequent cue and target responses.Backward inhibition is posited to aid task switching by counteracting the tendency to repeat a recent task. Evidence that factors such as cue transparency affect backward inhibition seems to imply that it is generated during task preparation, making its absence following trials on which a prepared task was not performed  surprising. However, the nogo method used in previous studies might have prevented detection of preparation-driven effects. We used a truncated-trial method instead, omitting stages of a trial with no need for a nogo signal. In Experiment 1, an The online version contains supplementary material available at 10.1007/s00426-022-01780-x. This article asks when backward inhibition is triggered: is it triggered proactively, in advance of difficulty resulting from shifting goals, or reactively, in response to difficulty resulting from shifting goals?The environment that we live in is complex, providing a multitude of options for actions that can be performed. Our goals also shift frequently, such that what is an appropriate action from one moment to the next differs. Mayr and Keele  proposedBraver and colleagues proposed two distinct modes of cognitive control Braver,  that makA and CBA . They found that responses were slower on trial n  of the ABA trial sequences relative to the CBA trial sequences; this difference is known as the n − 2 repetition cost. Mayr and Keele suggested that this cost is caused by the inhibition put in place on trial n − 1, to prevent interference from the task completed on trial n − 2, needing to be overcome on ABA sequences but not CBA sequences. In Experiment 3, they removed irrelevant distractors from the target screen, so that only one task could be performed on each trial. Although participants could perform the task correctly without a task cue, the researchers still presented task cues and instructed participants to use them explicitly in one condition, whereas participants were not presented with task cues in the other condition. They found an n − 2 repetition cost in the former condition but not in the latter, indicating that backward inhibition depends on proactive control. We also found similar results . They replaced nogo trials with cue-only trials, in which participants were presented only with a task cue, but without a target stimulus. Thus, cue-only trials truncated a trial rather than informing the participant to stop as on nogo trials. Lenartowicz et al. examined the switch cost , rather than the n − 2 repetition cost, and they found that there was no significant switch cost when the previous trial was a nogo trial, but there was still a significant switch cost when the previous trial was a cue-only trial. The researchers suggested that the nogo signal might have interfered with the effects of task preparation and abolished the subsequent switch cost.However, there might be a problem with drawing such a conclusion from these studies that used nogo trials because it might be that the presentation of a nogo tone eliminated backward inhibition triggered by task preparation, rather than the exclusion of response selection or execution stages of task processing preventing it from being triggered. Lenartowicz et al.  used whan − 2 repetition cost in Schuch and Koch’s , and on trials requiring no response we simply ended the trial without a go tone  and therefore with no need to present a nogo tone  as in Lenartowicz et al.’s  is particularly difficult because the episodic memory trace for the previous event interferes with processing of information for the current trial. In the experiments described here, we do not attempt to distinguish possible causes of the n − 2 repetition cost but instead simply aim to determine which trial events are able/required to trigger it. Thus, we cannot rule out the possibility that the n − 2 repetition cost that we use as our measure of backward inhibition may, at least in part, be measuring an interference effect rather than an inhibition effect. We return to this issue below.At this point, it is important to note that while the t test on data from trials following truncated trials to see if ABA trial sequences had worse performance than CBA sequences (i.e. an n − 2 repetition cost). To show that the experimental setup could reliably produce backward inhibition, we also tested for backward inhibition following completed trials, again with a one-tailed t test. This would be especially important for the interpretation of any absent n − 2 repetition cost following truncated trials.The four experiments were designed to ask similar questions and therefore we have conducted the same analysis within each. Our main question in each experiment was whether the stages of task processing remaining on the truncated trials  were sufficient to trigger backward inhibition that could be observed on the subsequent trial. We addressed this question by running a one-tailed t test to examine if the n − 2 repetition cost following completed trials was larger than the n − 2 repetition cost following the truncated trials.It has been suggested that backward inhibition is a flexible mechanism that is applied to the aspects of the task context that cause interference  and the preregistration for Experiment 4 did not include a comparison of the size of the n − 2 repetition costs between completed and cue-only trials. We have chosen to present the results as outlined above for all four experiments to directly and consistently address our key questions. The deviation from the preregistration is reported in the relevant “Results” section, and the additional tests of our key hypotheses that were not preregistered are clearly described as such in the relevant “Results” section.For Experiments 3 and 4, we preregistered some or all of these comparisons (see “Data availability” section). With hindsight, these preregistered analyses do not seem optimal: specifically, the preregistration for Experiment 3 did not specify that the test for the t tests for analysis of simple main effects of trial sequence where the interaction between trial sequence and previous trial completion was significant. We note that while the ANOVA method provides a potentially useful view of the overall pattern of results, it does not provide a direct test of our key questions because it would not allow us to test for the trial-sequence effect following just cue-only trials unless a significant interaction had first been found. In addition, while tests of the interaction effect in the ANOVA and the follow-up tests of simple main effects might appear to be completely equivalent to the one-tailed t tests, this is not the case because the former tests are not directional, so only half of their statistical power is available to test our hypotheses. In contrast, the one-tailed t tests look for specific, directional effects that can directly answer our research questions: whether there is a cost of n − 2 task repetition following either cue-only or completed trials; and whether there is an increase in cost following completed versus cue-only trials.Finally, to explore the data further, we also ran a conventional repeated-measures analysis of variance  in all experiments, with the factors of trial sequence  and previous trial completion , and with two-tailed no-execution trials ended 1500 ms after target onset without a response and replaced Philipp et al.’s nogo trials. In the remaining completed trials, we used a tone  as these trials required a response, which is equivalent to Philipp et al.’s go trials. A response signal occurred after a delay of either 100 or 1500 ms from target onset and participants were instructed not to respond prior to hearing the response signal. As a consequence, we had three types of trial: no-execution trials  and two types of completed trials . To have a sensitive measure of backward inhibition, as Philipp et al. did to explore the effects of response execution in the previous trial, we only analysed completed trials with a 100 ms response-signal delay that followed either no-execution trials or 1500 ms response-signal-delay completed trials.This experiment examined whether response execution is necessary to trigger backward inhibition, as suggested by Philipp et al.,  after thn − 2 repetition cost following completed trials, but not following no-execution trials, if response execution generates backward inhibition . Participants started with a practice block of 19 trials, which involved switching between all three tasks and included no-execution trials. The experiment itself consisted of 14 blocks of 84 trials each. In Philipp et al.’s design, 25% of all trials were nogo trials, and the ratio of 100 and 1500 ms signal delay for both go and nogo trials was 50:50. We chose not to include no-execution trials with a 100 ms signal delay, because with the truncated-trial method these were extremely short trials and proved to be confusing. Hence, the ratio of numbers of trials in each of the three trial types, no-execution: 1500 ms-delayed-response-completed: 100 ms-delayed-response-completed, was 1:3:3.Participants were informed of the category-response key mappings orally and visually, with a sheet depicting the mappings placed in front of them for the whole experiment. Participants sat at a comfortable distance from the computer which was running E-Prime 2.0 software , feedback was then presented for 500 ms in the centre of the black screen in magenta . Response time (RT) was recorded on completed trials as the interval between the onset of a response signal and the pressing of a response key.n − 2 was completed  to ensure that previous no-execution and previous completed trial sequences only differed with respect to trial n − 1.Mean RT and percentage of trials with an error (PE) were computed for each participant for completed trials with 100-ms response delay that followed a no-execution trial and that followed a completed trial with 1500-ms response delay. We only included trials where trial n − 2 and n were not both completed trials; if the response of either of the previous two trials (n − 2 and n − 1) was inaccurate then that trial (n) was excluded; and for the RT analysis the current completed trial was excluded if the response was incorrect . Additionally, trials were excluded if RT was below 200 ms or above 2500 ms , as was any trial where a response was given prior to the response signal being played . For the RT analysis, after exclusions there were on average 36 trials per trial sequence  per participant for previous no-execution trial sequences and on average 72 trials for previous completed trial sequences.Trial exclusions were as follows: the first two trials of every block were excluded from analysis; trial sequences were excluded if trials t tests to see if ABA trial sequences had worse performance than CBA trial sequences when trial n − 1 was completed and when trial n − 1 was no-execution, to find out if each type of previous trial completion causes significant backward inhibition in and of themselves. If response execution is necessary for backward inhibition to be triggered, we would expect to see no n − 2 repetition cost following no-execution trials. Additionally, a one-tailed paired t test was run to examine whether the n − 2 repetition cost following completed trials was larger than the n − 2 repetition cost following no-execution trials. Finally, an exploratory a two-way repeated-measures ANOVA was run with two within-subject factors, trial sequence  and trial n − 1 completion . See Fig. As outlined in “Introduction”, we ran one-tailed paired n − 2 repetition cost following no-execution trials (33 ms) was significant, t(39) = 2.26, p(one-tailed) = 0.015, dz = 0.36, indicating that response execution is not necessary to trigger backward inhibition. Unexpectedly, the n − 2 repetition cost following completed trials (7 ms) was not significant, t(39) = 0.71, p(one-tailed) = 0.242, dz = 0.11. The n − 2 repetition cost following completed trials was not significantly larger than that following no-execution trials (a mean difference of − 26 ms), t(39) = − 1.45, p(one-tailed) = 0.922, dz = − 0.23, indicating that response execution did not increase the strength of inhibition triggered.The F = 5.59, MSE = 2826, p = 0.023, n − 1 completion was not significant, F = 0.09, MSE = 8238, p = 0.762, n − 1 completion was not significant, F = 2.10, MSE = 3263, p = 0.156, The main effect of trial sequence in the ANOVA was significant, n − 2 repetition cost following no-execution trials (0.07%) was not significant, t(39) = 0.09, p(one-tailed) = 0.465, dz = 0.014, and neither was the n − 2 repetition cost following completed trials (− 0.45%), t(39) = − 0.87, p(one-tailed) = 0.806, dz = − 0.138. The n − 2 repetition cost following completed trials was not significantly larger than that following no-execution trials (a mean difference of − 0.52%), t(39) = − 0.52, p(one-tailed) = 0.697, dz = − 0.082.The F = 0.19, MSE = 7.70, p = 0.666, n − 1 completion was not significant, F = 1.41, MSE = 16.77, p = 0.242, n − 1 completion was not significant, F = 0.27, MSE = 9.98, p = 0.605, The main effect of trial sequence in the ANOVA was not significant, n − 2 repetition cost, but it is consistent with studies indicating a key role for cue-related processing in the existence and size of the effect  between response on trial n − 1 and task cue presentation on trial n contributed to the measure of backward inhibition following completed trials being unreliable in Experiment 1; Scheil and Kleinsorge . The results showed significant backward inhibition after no-execution trials, suggesting that response execution is not required. This result contradicts Philipp et al.’s  finding einsorge  found thn − 2 cost detected after no-execution trials in this experiment reflects to some extent the effect of episodic mismatch on ABA trials, whereby encountering the same task but with different trial details (target stimulus and/or response) creates interference and thereby slows responding. Because we followed Philipp et al.’s (n − 2 cost measured here. (We note that in subsequent experiments there was no such restriction imposed upon on targets with re-occurring tasks.)It is possible that the et al.’s  design fn − 2 repetition cost, participants had been able to prepare the cued task, process a target stimulus according to the cued task and select the appropriate response. Logically therefore, backward inhibition could have been triggered at (or prior to) any of those stages. To draw their conclusion that task preparation did not trigger backward inhibition, Schuch and Koch  should increase the size of the n − 2 repetition cost already present following preparation. In contrast, if backward inhibition is only ever triggered by target/response processing, then the preparation stage will not trigger it and we would expect to see an n − 2 repetition cost only following completed trials.Furthermore, if preparation is the only source of backward inhibition within a trial, then we would expect no difference in the size of the n − 2 repetition costs in our lab and which would increase our confidence in producing n − 2 repetition costs again , we replaced the number-judgement tasks with three tasks based on judgements of coloured shapes that have repeatedly produced Prosser, . We alsoForty participants were tested in total for course credit. One participant was excluded for having an accuracy rate of less than 70% and six participants were excluded for having more than 10% of response times faster than 200 ms or slower than 2000 ms. The upper bound was decreased from that used in Experiment 1 due to easier tasks being used. Of the 33 remaining participants 24 were female and their age ranged from 18 to 33 years old (mean age: 20.73 years).For this experiment and all following experiments, we used a colour-judgement task , a shape-judgement task  and a line-orientation-judgement task . The task cue words were COLOUR, SHAPE, or LINE, shown in the centre of the screen. The targets were trivalent, consisting of coloured shapes that contained two lines in one of the four orientations. All stimulus combinations were possible and the combination of the colours, shapes and line orientation was completely random and therefore stimulus repetitions were allowed. The index finger of the dominant hand was used for all responses. It was rested on the middle of the three buttons between trials and moved left and right to the outer buttons to respond to targets. Task repetitions were not allowed. Roughly 28% of all experimental trials were cue-only trials, and cue-only trials could only occur after a completed trial.All the instructions were given on the screen. Participants initially started with a practice block of each task separately which each lasted for 20 trials. They then completed a practice block of 20 trials where they switched between the three tasks. The final practice block replicated an experimental block, in that participants completed 50 trials that involved switching between the three tasks and included cue-only trials.Participants then completed the experimental blocks, of which there were 15 containing 50 trials each. After each block they were given the option to take a break and were given feedback of their average reaction time and total accuracy for that block to try to encourage better performance.A trial began with a task cue being presented for 1000 ms, as seen in Fig. n and n − 2 were completed trials and trial n − 1 was either a completed trial or a cue-only trial. The criteria for trial exclusion were the same as Experiment 1 except that the upper limit for trial exclusions based on reaction times was changed from 2500 to 2000 ms; 2.35% of all completed trials were excluded for having RTs below 200 ms or above 2000 ms. The percentage of trials with an inaccurate response on the current completed trial was 8.38%. For the RT analysis, after exclusions, there were on average 87 trials per trial sequence  per participant for previous cue-only trial sequences and on average 71 trials for previous completed trial sequences.The data were analysed essentially in the same manner as in Experiment 1. The planned analysis involved trial sequences where trials t tests to see if ABA trial sequences had worse performance than CBA trial sequences when trial n − 1 was cue-only and when trial n − 1 was completed, to find out if each type of previous trial completion causes significant backward inhibition. If preparation is enough for backward inhibition to be triggered, we would expect an n − 2 repetition cost following cue-only trials . Additionally, a one-tailed paired t test was run to examine whether the n − 2 repetition cost following completed trials was larger than the n − 2 repetition cost following cue-only trials. Finally, an exploratory two-way repeated-measures ANOVA was run with two within-subject factors, trial sequence  and trial n − 1 completion . See Fig. As with Experiment 1 we ran one-tailed paired n − 2 repetition cost following cue-only trials , t(32) = − 2.27, p(one-tailed) = 0.985, dz = − 0.40, indicating that task preparation was not enough to trigger backward inhibition. There was a significant n − 2 repetition cost following completed trials, t(32) = 3.43, p(one-tailed) < 0.001, dz = 0.60, with ABA trial sequences being 33 ms slower than CBA trial sequences, indicating that completing all stages of task processing was enough to trigger backward inhibition. The n − 2 repetition cost following completed trials was significantly larger (a mean difference of 58 ms) than that following cue-only trials, t(32) = 5.36, p(one-tailed) < 0.001, dz = 0.93.There was no significant F = 0.24, MSE = 2538, p = 0.627, n − 1 completion was significant, F = 144.47, MSE = 4742, p < 0.001, n − 1 completion was significant, F = 28.76, MSE = 963, p < 0.001, t tests showed that effect of trial sequence following completed trials  was again significant, p(two-tailed) = 0.002; however, following cue-only trials, the effect of trial sequence was in the opposite direction, with a 25 ms benefit (rather than a cost) of n − 2 repetition being significant with the two-tailed test, p(two-tailed) = 0.030.The main effect of trial sequence in the ANOVA was not significant, n − 2 repetition cost following cue-only trials (− 0.99%) was not significant, t(32) = − 1.72, p(one-tailed) = 0.953, dz = − 0.30. The n − 2 repetition cost following completed trials (0.25%) was not significant, t(32) = 0.35, p(one-tailed) = 0.366, dz = 0.06. The n − 2 repetition cost following completed trials was not significantly larger than that following cue-only trials (a mean difference of 1.23%), t(32) = 1.45, p(one-tailed) = 0.079, dz = 0.25.The F = 0.57, MSE = 7.85, p = 0.455, n − 1 completion was not significant, F = 0.20, MSE = 32.85, p = 0.656, n − 1 completion was not significant, F = 2.09, MSE = 5.98, p = 0.158, The main effect of trial sequence in the ANOVA was not significant, n − 1 was completed, there was a significant n − 2 repetition cost. However, when trial n − 1 was a cue-only trial, there was no significant n − 2 repetition cost, and in fact the exploratory analysis found the effect was significant in the opposite direction, that is, there was a significant n − 2 repetition benefit. The n − 2 repetition benefit is likely caused by the task completed on trial n − 2 still being active when the participant performs the task on trial n in an ABA sequence. This implies that no inhibition of that task had been applied during the (n − 1) cue-only trials. Hence, it appears that the preparation stage did not trigger backward inhibition in this experiment.Experiment 2 investigated whether the preparation stage is sufficient to trigger backward inhibition. When trial n − 2 repetition cost on the next trial. In contrast, completing the task  clearly was sufficient to generate the cost. Putting these results together with those from Experiment 1, it seems that the key stage occurred after target onset but before response execution, consistent with the proposal that response selection is responsible for driving the effect . What is clear is that whatever processes did take place prior to target onset, they were insufficient to drive any detectable This experiment was the same as Experiment 2 apart from the task cues were no longer the name of the task and instead they were "*****" for the colour task, "@@@@@" for the line-orientation task and "&&&&&" for the shape task.Forty-eight participants were tested. Three participants were excluded for having an accuracy rate of less than 70%, and none were excluded for having more than 10% of response times faster than 200 ms or slower than 4000 ms.This experiment was the same as Experiment 2 apart from the task cues were no longer the name of the task and instead they were “*****” for the colour task, “@@@@@” for the line-orientation task and “&&&&&” for the shape task. Additionally, due to software capabilities the response-to-cue interval was increased from 50 to 100 ms to allow for end of trial processing, and we removed the 50 ms blank following task cue presentation on cue-only trials.n and n − 2 were completed trials and trial n − 1 was either a completed trial or a cue-only trial. The criteria for trial exclusion were the same as Experiment 1 except that the upper limit for trial exclusions based on reaction times was changed from 2500 to 4000 ms . The percentage of trials with an inaccurate response on the current completed trial was 9.66%. For the RT analysis, after exclusions there were on average 87 trials per trial sequence  per participant for previous cue-only trial sequences and on average 67 trials for previous completed trial sequences.We analysed the mean RT for each participant and mean percentage of trials on which an error was made. The planned analysis involved trial sequences where trials t test to compare ABA trial sequence to CBA sequences following cue-only trials because we had wished to be able to test for the presence of a significant n − 2 repetition benefit (as found in Expt. 2) as well as testing for a potential n − 2 repetition cost. In hindsight, we realise that this would not allow us to consistently prioritise the central research question regarding backward inhibition , so we report the results for a one-tailed test (rather than the preregistered two-tailed test) for an n − 2 repetition cost following cue-only trials as we have done for the other experiments in this paper. An exploratory (and not preregistered) two-way repeated-measures ANOVA was also run, with two within-subject factors, trial sequence  and trial n − 1 completion . Please note that the two-tailed test for an effect of trial sequence following cue-only trials that had been preregistered is still reported here as it equates to the test for the simple main effect of trial sequence following cue-only trials stemming from the ANOVA. See Fig. For this experiment and the following experiment, unlike the previous two experiments, we preregistered our plan for data processing and statistical analysis (see “Data availability” ). The analysis plan outlined in the introduction, and followed below, involves one deviation from the preregistration. We had preregistered a two-tailed (rather than one-tailed) n − 2 repetition cost , t(44) = − 5.01, p(one-tailed) > 0.999, dz = − 0.75, indicating that task preparation was not enough to trigger backward inhibition. Following completed trials, there was a significant n − 2 repetition cost (63 ms), t(44) = 3.76, p(one-tailed) < 0.001, dz = 0.56, indicating that that completing all stages of task processing was enough to trigger backward inhibition. The n − 2 repetition cost following completed trials was significantly larger (a mean difference of 140 ms) than that following cue-only trials, t(44) = 6.32, p(one-tailed) < 0.001, dz = 0.94.Following cue-only trials, there was a not a significant F = 0.38, MSE = 6112, p = 0.539, n − 1 completion was significant, F = 317.05, MSE = 11,863, p < 0.001, n − 1 completion was significant, F = 39.99, MSE = 5514, p < 0.001, t tests showed that the trial-sequence effect following completed trials  remained significant, p(two-tailed) < 0.001, and that (as in Experiment 2) the trial-sequence effect was in the opposite direction following cue-only trials, an n − 2 repetition benefit of 77 ms being significant with the two-tailed test, p(two-tailed) < 0.001.The main effect of trial sequence in the ANOVA was not significant, n − 2 repetition cost following cue-only trials (0.17%), t(44) = 0.28, p(one-tailed) = 0.390, dz = 0.04. There was no significant n − 2 repetition cost following completed trials (0.73%), t(44) = 1.06, p(one-tailed) = 0.148, dz = 0.16. The n − 2 repetition cost following completed trials was not significantly larger than that following cue-only trials (a mean difference of 0.56%), t(44) = 0.61, p(one-tailed) = 0.272, dz = 0.09.There was no significant F = 0.98, MSE = 9.19, p = 0.329, n − 1 completion was significant, F = 20.14, MSE = 11.10, p < 0.001, n − 1 completion was not significant, F = 0.373, MSE = 9.37, p = 0.544, The main effect of trial sequence in the ANOVA was not significant, n − 2 repetition cost when trial n − 1 was completed, but not when trial n − 1 was a cue-only trial. In fact, the n − 2 repetition effect constituted a benefit following cue-only trials, which was statistically significant in the exploratory analysis. As mentioned in the discussion of Experiment 2, the significant n − 2 repetition benefit is likely caused by activation from the task on trial n − 2 remaining and not being dampened by inhibition applied on trial n − 1. This result speaks against our suggestion above that it might have been the absence of a need for active task maintenance during the preparation interval that led to no backward inhibition being evident following cue-only trials in Experiment 2. Instead, despite the use of arbitrary cues in Experiment 3, we saw again a benefit of n − 2 repetition rather than the cost that would have indicated backward inhibition. Thus far, therefore, the evidence from these studies points towards backward inhibition being generated solely via processes occurring after target onset but before response execution , they did not need to process it in detail prior to the target being presented. If participants did not use the task cue to prepare in advance, then finding no backward inhibition following cue-only trials would not constitute evidence that preparation is insufficient to trigger backward inhibition.This final experiment used a double-registration paradigm  and target-driven retrieval of task information . Assuming that we replicate Regev and Meiran’s  and target-driven retrieval (target responses) or just preparation-driven retrieval (cue responses).In addition to the issue of whether preparation triggers backward inhibition, the present double-registration procedure also examined whether the inhibition that is triggered on the preceding trial affects cue responses or target responses on the current trial. Regev and Meiran  used theMeiran’s  results Forty-nine participants were tested in total for course credit. Two participants were excluded for having an accuracy rate of less than 70% on cue responses, three participants were excluded for having an accuracy rate of less than 70% on target responses, and a further two participants were excluded for having more than 10% of trials excluded from analysis for response times faster than 200 ms or slower than 2000 ms on cue responses. No participants were excluded for having more than 10% of trials excluded from analysis for response times faster than 200 ms or slower than 3000 ms on target responses.This experiment was based on Experiment 2, the key difference being that in this experiment participants responded to the task cue as well as the target. The responses were made on MasterCooler keyboards (msec-accuracy responses). For the cue responses, participants used their left hand and pressed buttons S , D  and F . For the target responses, participants used their right hand and pressed buttons J  and K . Unlike previous experiments the background was dark grey  rather than black or white.The experimental set-up was the same as Experiment 2 apart from the following changes. The final practice block and the experimental blocks contained 62 trials, rather than 50 trials. During the break between blocks, when participants were given their average RT and their accuracy, participants were given extra feedback during the break that stated, “Remember, please try to be fast AND accurate!” if they made more than nine errors in that block.A trial began with a task cue being presented for 200 ms .n − 1 as cue-only trials, and 135 were CBA trial sequences with trial n − 1 as cue-only trials. Of all the completed trials, 50% were ABA and 50% were CBA.There were 60 analysable trials within each block , exactly 18 of those trials (30%) being cue-only, which resulted in 270 cue-only trials within the whole experiment. Cue-only trials were always followed by completed trials. In total across the session, exactly 135 trials were ABA trial sequences with trial n and n − 2 were completed trials. If either the cue response or target response of either of the previous two trials (n − 2 and n − 1) was inaccurate then that trial (n) was excluded. For analysis of the cue and target responses the trial was excluded if the response time for the task cue was below 200 ms or above 2000 ms  and for RT analysis the whole trial would be excluded if the cue response was inaccurate . For analysis of the target response, the trial was excluded if the response time for the current target response was below 200 ms or above 3000 ms  and for the RT analysis the current trial would be excluded if the target response was inaccurate . For the RT analysis at cue responses, there were on average 101 trials per trial sequence per participant on previous cue-only trial sequences, and 69 trials per trial sequence per participant on previous completed trial sequences, after exclusions. For the RT analysis at target responses, there were on average 92 trials per trial sequence per participant on previous cue-only trial sequences, and 63 trials per trial sequence per participant on previous completed trial sequences, after exclusions.The plan for data processing (including criteria for data exclusion) and analysis for this experiment was preregistered (see “Data availability” section) and was carried out without deviation, as follows. We analysed the mean RT and PE for each participant. The first two trials of every block were excluded, and trial sequences were only included if trials t tests were run to see if there was an n − 2 repetition cost following cue-only trials, one on cue-response data and one on target response data, both testing whether backward inhibition  could be triggered by preparation on the preceding trial. Two further planned one-tailed paired t tests were run to compare ABA and CBA trial sequences when trial n − 1 was completed, one on cue response data and one on target response data, testing whether we would replicate Regev and Meiran’s , that compared the n − 2 repetition cost following cue-only trials to the n − 2 repetition cost following completed trials, at cue responses and target responses. This analysis was to look at whether performance on the previous trial  significantly increased the size of backward inhibition as compared to when only preparation could occur on the previous trial . This analysis is followed by an exploratory (and not preregistered) two-way repeated-measures ANOVA with two within-subject factors, trial sequence  and trial n − 1 completion , run at each response level.We also ran two one-tailed paired n − 2 repetition cost of 14 ms following cue-only trials for cue responses, t(41) = 1.82, p(one-tailed) = 0.038, dz = 0.28, indicating that completing task preparation was enough to trigger backward inhibition. There was also a significant n − 2 repetition cost of 44 ms following completed trials, t(41) = 5.95, p(one-tailed) < 0.001, dz = 0.92, indicating that completing all stages of task processing was enough to trigger backward inhibition. The n − 2 repetition cost following completed trials was significantly larger (a mean difference of 30 ms) than that following cue-only trials, t(41) = 3.30, p(one-tailed) = 0.001, dz = 0.51, indicating that stages after task preparation increased the strength of backward inhibition triggered.There was a significant F = 22.66, MSE = 1550, p < 0.001, n − 1 completion was not significant, F = 0.15, MSE = 5605, p = 0.697, n − 1 completion was significant, F = 10.89, MSE = 849, p = 0.002, n − 2 repetition cost of 44 ms, reported above with a one-tailed test) was significant when tested with a two-tailed t test, p(two-tailed) < 0.001; the effect of trial sequence following cue-only trials (an n − 2 repetition cost of 14 ms), was not significant with a two-tailed test, p(two-tailed) = 0.077.Analysis of variance showed a significant main effect of trial sequence, n − 2 repetition cost following cue-only trials (− 13 ms) for target responses, t(41) = − 0.83, p(one-tailed) = 0.795, dz = − 0.13, indicating that task preparation was not enough to trigger backward inhibition. There was a significant n − 2 repetition cost following completed trials (31 ms), t(41) = 3.02, p(one-tailed) = 0.002, dz = 0.47, indicating that completing all the stages of task processing was enough to trigger backward inhibition. The n − 2 repetition cost following completed trials was significantly larger (43 ms) than that following cue-only trials, t(41) = 2.73, p(one-tailed) = 0.005, dz = 0.42.There was no significant F = 0.73, MSE = 4485, p = 0.397, n − 1 completion was significant, F = 4.40, MSE = 10,859, p = 0.042, n − 1 completion at target response was significant, F = 7.48, MSE = 2640, p = 0.009, n − 2 repetition cost following completed trials (reported above with a one-tailed test) remained significant with the two-tailed test, p(two-tailed) = 0.004, whereas there was no significant effect of trial sequence following cue-only trials, the numerical n − 2 repetition benefit of 13 ms not being statistically significant, p(two-tailed) = 0.409.The main effect of trial sequence in the ANOVA was not significant, n − 2 repetition cost following cue-only trials in the error data (1.96%), t(41) = 2.51, p(one-tailed) = 0.008, dz = 0.39; this is consistent with the significant cost in the RT data, indicating that task preparation is sufficient to trigger backward inhibition affecting cue responses. There was a significant n − 2 repetition cost following completed trials (2.01%), t(41) = 2.60, p(one-tailed) = 0.007, dz = 0.40. There was no significant increase in n − 2 repetition cost following completed trials as compared to that following cue-only trials (a mean difference of 0.05%), t(41) = 0.04, p(one-tailed) = 0.482, dz = 0.01.There was a significant F = 12.78, MSE = 12.89, p < 0.001, n − 1 completion was not significant, F = 0.77, MSE = 11.13, p = 0.385, n − 1 completion was not significant, F = 0.002, MSE = 12.44, p = 0.965, The main effect of trial sequence in the ANOVA was significant, n − 2 repetition cost following cue-only trials (− 0.28%), t(41) = − 0.40, p(one-tailed) = 0.654, dz = − 0.06. There was no significant n − 2 repetition cost following completed trials (0.19%), t(41) = 0.23, p(one-tailed) = 0.409, dz = 0.04. There was no significant increase of n − 2 repetition cost following completed trials as compared to that following cue-only trials (a mean difference of 0.47%), t(41) = 0.48, p(one-tailed) = 0.316, dz = 0.07.There was no significant F = 0.01, MSE = 13.91, p = 0.936, n − 1 completion was not significant, F = 1.31, MSE = 7.90, p = 0.259, n − 1 completion was not significant, F = 0.23, MSE = 9.77, p = 0.632, The main effect of trial sequence in the ANOVA was not significant, n − 2 repetition cost at cue responses in terms of both RT and errors. The RT cost was small in terms of absolute size (14 ms) and, while it was statistically significant using the one-tailed test (as preregistered), we note that it would not have been significant had we used a two-tailed test. We consider the one-tailed test to have been appropriate since we were looking specifically for evidence of backward inhibition and therefore a cost (rather than simply an effect) of trial sequence, and the one-tailed test offers greater statistical power (and protection against a Type 2 error) for that specific prediction. Nevertheless, as with any significant result it could of course represent a Type 1 error and conclusions based upon this result alone should be considered somewhat tentative for the time being.Unlike the preceding experiments, this experiment required responses to be made to task cues as well as to targets and therefore allowed us to demonstrate that participants prepared (at least to the extent of identification) the cued task on cue-only trials. Following cue-only trials, there was a significant n − 2 repetition cost following cue-only trials suggests that preparation can trigger backward inhibition. The fact that the cost occurred at cue responses indicates that the backward inhibition triggered by task preparation had the effect of slowing the process of identification of the task that had been switched away from previously. While this effect must have been triggered during the preparation part of the preceding trial , it would not constitute proactive control in the sense of acting in anticipation of, and to diminish, conflict expected to occur during future task performance. Rather, it seems likely to represent the persistence of inhibition generated to deal with conflict occurring at the time of task preparation, between alternative potential task-identity judgements. Nevertheless, it might well contribute to the standard backward inhibition effect measured in paradigms where only one response (to the target) is required per trial, especially if preparation times are short such that a slowing of task identification will impact upon the overall RT. It could be argued that there might have been no conflict at the task-identification stage if there had not been the requirement to signal the result of target identification via a specific overt response; in other words, the n − 2 repetition cost in cue responses might be an artefact resulting from using the double-registration procedure. This is an open question that would need to be addressed in future experiments. Notably, unlike the cost seen in Experiment 1 following no-execution trials (truncated after response selection), the cost seen here following cue-only trials (truncated after cue responses) could not result from an episodic mismatch effect, because all cue stimuli and cue responses for a particular task would repeat every time that task was used (i.e. there was no “mismatch” in this case that could cause interference). Hence, the n − 2 repetition cost does seem to represent inhibition in this case.The finding of a significant n − 2 repetition cost following cue-only trials in Expt. 4), it is unlikely that such a small effect would have been observable in the RTs measured relative to onset of the target in those earlier experiments. In Experiment 4 there was no analogous time-period (prior to the onset of the event from which cue-RTs were measured) during which this cost could have been overcome prior to it being measured. Finally, as noted above, we cannot rule out the possibility that the backward inhibition observed in cue responses in Experiment 4 resulted solely from competition between overt responses rather than from task-identification processes.In Experiments 2 and 3, we found no evidence of backward inhibition following cue-only trials, contrasting with the finding of backward inhibition in cue responses following cue-only trials in Experiment 4. A number of reasons might account for that discrepancy. First, in Experiments 2 and 3 we did not ensure that task preparation took place on cue-only trials; the absence of any subsequent backward inhibition might therefore simply result from preparation not having occurred, rather than indicating that preparation did not drive backward inhibition. In Experiment 4, in contrast, we required responses to cues to identify the cued task, ensuring that at least an element of task preparation did take place. Second, in Experiments 2 and 3 RTs were measured from the time of onset of the target stimulus, but following a 1000 ms cue-target interval in each case. So if preparation on the preceding trial had triggered backward inhibition that slowed the process of cue-interpretation . The presence of a significant cost at target responses following completed but not cue-only trials might indicate that task performance triggers an additional element of inhibition, affecting the use of task information to make a target response that is not triggered by preparation alone. However, we might expect that with no inhibition at all we should see a significant n − 2 repetition benefit (as in Experiments 2 and 3), whereas no significant benefit was present at target responses following cue-only trials . The absence of either a significant n − 2 repetition cost or benefit might indicate that just enough inhibition was triggered to overcome the activation from the preceding trial, but not enough inhibition to be detrimental to performance on the subsequent trial . This effect was small in terms of RT  and therefore may be considered tentative at present, although we note that it was mirrored by a significant effect in errors. There was neither a cost nor a benefit of n − 2 task repetition following cue-only trials in target responses. Across Experiments 2–4, there was robust evidence that backward inhibition following completed trials, on which both task preparation and task performance took place, was stronger than any following cue-only trials, on which only task preparation took place.This set of experiments investigated which stage of task processing triggers the backward inhibition effect. We used a truncated-trial design to exclude stages of processing from taking place on trial The finding of backward inhibition after no-execution trials in Experiment 1 differs from that of Philipp et al. , who foun − 2) competes  and is therefore suppressed, causing slowing of the same process on trial n of ABA sequences. The method used in Experiment 4 enabled us to test the implication of these studies—that backward inhibition may be triggered during cue processing—more directly than is possible when all trials are completed. The cue-only version of the truncated-trial method allows us to rule out all trial events that would usually occur after task preparation (target and response processing) as being the triggers of a subsequently observed effect, and the finding of an n − 2 repetition cost following cue-only trials is consistent with cue processing directly triggering a degree of backward inhibition, although we cannot at this stage distinguish between the roles of processing the cue itself and processing the cue response in generating the effect. The double-registration method allows us to determine whether inhibition impacts upon cue processing or target processing. Regev and Meiran  rather than task performance. The absence of neither a significant cost nor benefit of n − 2 task repetition in target responses following cue-only trials is an ambiguous result that is potentially consistent with a degree of backward inhibition , suggesting that more inhibition − and possibly inhibition of additional processes − was applied on completed trials in comparison with cue-only trials.In contrast to the tentative nature of the evidence for preparation-driven backward inhibition, we found evidence in all our experiments, including Experiment 4, that backward inhibition can be applied during task performance, and the relatively larger and more statistically robust costs following completed than cue-only trials seen in Experiments 2–4 suggests that backward inhibition generated during task performance may be much more substantial than any generated during task preparation. In Experiment 4, the n − 2 repetition cost on target responses following completed and cue-only trials was due to task performance occurring only on completed trials. First, completed trials were roughly 975 ms longer than cue-only trials. This increased length, rather than the addition of performance per se, might potentially have allowed more inhibition to be applied to the competing task during completed trials than during cue-only trials. Second, the degree of task preparation required to respond to a task cue might not be the same as that required to prepare fully to complete a task, and so it might be that more preparation occurred after the response to the task cue was given; if so, it might mean that more preparation occurred on completed trials than on cue-only trials . Since such a mismatch effect could only occur with repetition of a recent task (e.g. ABA sequences), it can potentially inflate or even account for the n − 2 repetition cost Below is the link to the electronic supplementary material.


The aim of the present study was to predict the vault of implantable collamer lens (ICL) using a formula established based on the preoperative parameters.This retrospective study included data on 226 eyes from 114 patients who underwent EVO-ICL surgery between May 2021 and April 2022. Preoperative parameters, such as anterior chamber depth (ACD) horizontal angle-to-angle diameter (ATA), Crystalline lens rise (CLR), and vault (1 week after surgery) were measured by anterior segment optical coherence tomography. The axial length of eyes (AL) and white-to-white (WTW) values were measured using an IOLmaster and calipers under a slit lamp separately.2 = 0.39, P < 0.001).The mean WTW, ACD, horizontal ATA diameter, CLR, AL, and vault 1 week after surgery were 11.42 ± 0.37 mm, 3.25 ± 0.25 mm, 11.72 ± 0.44 mm, 45.78 ± 175.42 μm, 27.61 ± 1.93 mm, and 586.51 ± 254.54 μm respectively. Multivariate regression analysis showed that the ICL-width, ACD (β = 0.293), ATA (β =-0.657), CLR (β =-0.157), and the anterior chamber angle in temporal side (T:ACA) (β=-0.277) were predictors of the vault size (adjusted-RThe formula built based on these preoperative parameters could be used to predict the vault before surgery. The predictors indicated that the pre-operative parameters of eye such as ACD, ATA, CLR and T:ACA play an important role in choosing optimal ICL size. Phakic posterior chamber lens(PC-pIOLs) are a common surgical solution for correcting myopia with few side effects. Among the different types of PC-pIOL available in the market, implantable collamer lens (ICL) that represents the most studied PC-pIOL is increasingly becoming a first-choice option for refractive surgery in patients with high myopia . With dehttps://ocos.staarag.ch/landing/). In addition, the sulcus-to-sulcus distance (STS), measured using high-frequency ultrasound biomicroscopy, may be ideal for ICL size calculation [An appropriate size of ICL should be chosen based on several eye parameters for optimal vault outcomes. For example, the white-to-white (WTW) and anterior chamber depth (ACD) are used for calculating the optimal ICL size using the Online Calculation and Ordering System (OCOS)  ACD > 2.8 mm, and (iii) completed EVO-ICL surgery and follow-up in this hospital. The exclusion criteria were (i) the presence of other ocular conditions, such as cataracts, glaucoma, and corneal dystrophy; (ii) systemic diseases such as diabetes, autoimmune disease, and other diseases that can influence the parameters after surgery, and (iii) patients with missing data or who were not suitable for preoperative or postoperative examination. Based on these criteria, 226 eyes from 114 patients were selected.This retrospective study included 226 eyes of 114 patients  who underwent EVO-ICL surgery to treat myopia at Zhongshan Ophthalmic Center between May 2021 and April 2022 by two senior surgeons. The indications for ICL surgery were pre-operative refractive error of all patients recruited in this study and the data of pre-operative refractive error was presented in table ICL sizes were selected using the Online Calculation and Ordering System provided by STAAR Surgical based on WTW distance and ACD. Both the toric EVO-ICL and non-toric EVO-ICL procedures were included.Pentacam  was performed to exclude keratoconus, evaluate the curvature and other parameters of the cornea, roughly determine the axis of corneal astigmatism and the depth of the anterior chamber depth. A swept-source AS-OCT instrument  was used to measure the ACD, ATA, CLR, pupil size, postoperative vault, and the nasal (N:) and temporal (T:) anterior chamber angle (ACA). The AS-OCT scans were performed along the horizontal meridian using a single scan centered on the pupil by one operator. AL was measured using an IOLmaster700 . Additionally, WTW was measured using a caliper under a slit lamp; the caliper’s lowest unit of measurement was 1 mm. All parameters were measured three times and the mean value was obtained. The parameters were defined as follows: WTW, the distance between the nasal and temporal limbus points between the white sclera; ACD, the distance between the anterior surface of the crystalline lens and the posterior surface of the cornea; The horizontal ATA, the distance between the angle recesses on the nasal and temporal sides; CLR, the anteroposterior distance between the anterior crystalline lens surface and the angle recess to angle recess line ; postopeOther parameters included were age and the size of the ICL selected using OCOS.Prior to surgery, compound tropicamide eye drops  were used for mydriasis. Surface anesthesia was performed using Proparacaine Hydrochloride (Alcaine™: s.a.ALCON-CONUVERUR n.v). The axis for corneal astigmatism was used as a marker prior to surgery. Sterile operations were employed during every single stage of the surgery. The main 3.2 mm incision was made at the steepest meridian of the cornea, then the ICL was injected into the anterior chamber using a manufacturer injector cartridge  after the viscoelastic material  had been placed into the anterior chamber and moved to the posterior chamber through the pupil. After correcting the position of the lens, the viscoelastic material was washed out using a balanced saline solution. After surgery, antibiotics  were applied topically 4 times a day for 4 weeks.All surgeries were successful, and no operative complications, such as pupillary block or intraocular pressure rises were observed. No patients required any extra surgery or exchange of the ICL lens.N ≥ L/f2 and where N represented the number of size, L = 19 and f2 = 0.15 when predictors were 12 and the size effect is medium. SPSS version 25 (IBM Corp.) was used for statistical analysis. Data normality was tested using Schapiro-Wilk test before statistical analysis. All parameters except the proportion of ICL-width were expressed as mean ± standard deviation (SD). A multivariate linear regression (MLR) analysis was performed to identify the relationship between vault and other pre-operative parameters. The predictor variables included age, AL, ATA, ACD, WTW, CLR, and the lens parameters, and the dependent variable was the vault. P < 0.05 was considered to indicate a statistically significant difference.Sample size was calculated by two-step rule-of thumb that Table ACA, CLR, pupil size, AL, and the parameters of ICL were used as predictor variables, and the postoperative vault was the dependent variable. Table The multivariate correlation analysis model could be described using the following formula: Dependent vault = A0 + A1 x X1 + A2 x X2 + A3 x X3+… + + An x Xn + ε, where A represents the MLR coefficients and X represents the predictor variables. The formula for vault prediction was: Vault = -1,825.6 + 380.3 x ICL width + 294.3 x ACD − 387.8 x ATA − 0.22 x CLR − 5.0 x TACA.(12.60 μm) = 2,738 + 294.3 x ACD − 387.8 x ATA − 0.22 x CLR – 5.0 x T:ACA; Vault(12.10 μm ) = Vault(12.60 μm) − 190.15; Vault(13.20 μm) = Vault(12.60 μm) + 228.18; or Vault(13.70 μm) = Vault(12.60 μm) + 418.33.Based on the ICL size, the formula could be modified as follows: VaultAccording to this formula, five preoperative parameters could be used to predict the postoperative vault.The vault is the most significant parameter correlated with postoperative complications and the need for re-surgery , 4. WhenThe size of the lens, also known as the ICL width, was positively associated with the vault, similar to that observed by Lee et al. . This maATA was negatively correlated with the vault. This may be explained in a similar manner to the ICL width. ATA can be regarded as the horizontal distance of the lens. When ATA is smaller, the force required to bend the lens forward towards the iris is stronger, resulting in a higher vault. Although STS should be the ideal parameter since it represents the horizontal situation of the ICL lens, the repeatability of STS measurement is not reliable , and STSRegarding the ACD, a positive correlation with vault was identified similar to that observed by Alfonso et al. , 19. A sPrevious studies have highlighted the role of CLR in ICL surgery , 11, 20.A regression analysis by Wen et al.  found a The other parameters were not significantly associated with the vault. However, it has been shown that age was negatively correlated with vault , and thiThis retrospective analysis built a formula that included five preoperative parameters that accounted for 39% of the vault variance. But there are some imitations in this study, first, for the participants are all Chinese, further studies are required with additional and larger sample sizes from different ethnic origin to better confirm the parameters, predict vault and to aid in the choice of the optimal ICL size. Second, the current study is a retrospective study, so another perspective study based on the current work could be promoted to provide higher-level evidence.In conclusion, this study established a new formula which could be used to predict the vault before ICL surgery. In addition to some parameters that were included in other formulas, this study showed that the T:ACA which was ignored before also played an important role in determining the vault.


The goal of this study was to investigate factors associated with 30-day readmission in a multivariate model, including the CDC wound classes “clean,” “clean/contaminated,” “contaminated,” and “dirty/infected.”The 2017–2020 American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP) database was queried for all patients undergoing total hip replacement, coronary artery bypass grafting, Ivor Lewis esophagectomy, pancreaticoduodenectomy, distal pancreatectomy, pneumonectomy, and colectomies. ACS-defined wound classes were concordant with CDC definitions. Multivariate linear mixed regression was used to determine risk factors for readmission while adjusting for type of surgery as a random intercept.p < .001), “contaminated” (p < .001), and “dirty/infected” (p < .001) wound classes (when compared to “clean”) were significantly associated with 30-day readmission. Organ/space surgical site infection and sepsis were among the most common reasons for readmission in all wound classes.477,964 cases were identified, with 38,734 (8.1%) patients having experienced readmission within 30 days of surgery. There were 181,243 (37.9%) cases classified as wound class “clean”, 215,729 (45.1%) cases classified as “clean/contaminated”, 40,684 cases (8.5%) classified as “contaminated”, and 40,308 (8.4%) cases classified as “dirty/infected”. In the multivariate generalized mixed linear model adjusting for type of surgery, sex, body mass index, race, American Society of Anesthesiologists class, presence of comorbidity, length of stay, urgency of surgery, and discharge destination, “clean/contaminated” (Wound classification was strongly prognostic for readmission in multivariable models, suggesting that it may serve as a marker of readmissions. Surgical procedures that are “non-clean” are at significantly greater risk for 30-day readmission. Readmissions may be due to infectious complications; optimizing antibiotic use or source control to prevent readmission are areas of future study.The online version contains supplementary material available at 10.1007/s00268-023-07093-3. Hospital readmissions after surgeries can be costly and unnecessary. To improve hospital care and penalize higher than predicted readmission rates, the Center for Medicare and Medicaid Services (CMS) created the Hospital Readmission Reduction Program , 2. DespWound class, a variable recorded in all surgeries and determined during the operation, is often used to estimate a patient’s risk for postoperative surgical site infection (SSI) –8. DefinThe objective of this study was to evaluate CDC wound classification as a prognostic factor for thirty-day readmission following surgery. We hypothesized that “clean/contaminated,” “contaminated,” and “dirty/infected” wound classes, when compared to “clean,” would have significantly greater risk for thirty-day readmission in a multivariate analysis of all total hip replacement, coronary artery bypass grafting (CABG), Ivor Lewis esophagectomy, pancreaticoduodenectomy, distal pancreatectomy, pneumonectomy, or colectomy in a nationwide surgical database.The ACS NSQIP, a large, national database of surgeries and up to 30-day postoperative outcomes, was utilized for this study . All patThe ACS NSQIP captures readmission status up to 30 days after the principal operation; this was used as the primary outcome of the study. Data on age, gender, body mass index (BMI), preoperative comorbidities, principal operation, wound classification, American Society of Anesthesiologists (ASA) class, 30-day readmission status, discharge destination, post-operative complications, hospital length of stay, urgency of surgery, suspected reason for readmission, and 30-day mortality were collected. Comorbidities indexed by ACS NSQIP include diabetes, current smoking status, ventilator dependency, history of chronic obstructive pulmonary disease, ascites, history of congestive heart failure, hypertension, acute renal failure, steroid use for chronic condition, bleeding disorders, and preoperative sepsis; patients with any of these diagnoses were considered to have comorbidity. Postoperative infectious complication was defined as presence of wound infection , pneumonia, urinary tract infection, or sepsis, as these are outcomes specifically captured by ACS NSQIP for each patient.Univariate analysis was conducted using Student’s t-test or Wilcoxon Rank Sum test for continuous variables and Chi-square test for categorical variables. Statistical significance was defined as p-value less than 0.05. The statistical programs “glm” and “glmer” with R version 4.2.1 were utilized to conduct the data analysis . Signifip = 0.075).There were 477,964 cases meeting inclusion criteria, and of those, 38,734 8.1%) patients experienced readmission within 30 days of surgery. There were 181,243 cases (37.9%) classified as “clean” wound class, 215,729 (45.1%) cases classified as “clean/contaminated,” 40,684 cases (8.5%) classified as “contaminated,” and 40,308 (8.4%) cases classified as “dirty/infected” , 6,986 (18.0%) had undergone a “clean” surgery, 22,145 (57.2%) had “clean/contaminated” surgery, 4,871 (12.6%) had “contaminated” surgery, and 4,732 (12.2%) had “dirty/infected” surgery. The top ten reasons for readmission were explored. Among patients readmitted for one of the top ten readmission reasons in our sample, the proportion of each of the top ten reasons by wound class is displayed in Fig. The associations between wound classification and organ/space SSI, deep SSI, superficial SSI, sepsis, or pneumonia in multivariate models were analyzed, as these outcomes were some of the most common reasons for readmission Table . CompareThis study of total hip replacement, CABGs, Ivor Lewis Esophagectomy, pneumonectomy, pancreaticoduodenectomy, distal pancreatectomy, and “dirty/infected” colectomy in the 2017–2020 ACS NSQIP database shows that dirtier wound classifications, when compared to “clean”, were at significantly higher risk for 30-day readmission. “Non-clean” wound classifications had one of the strongest adjusted odds ratios for 30-day readmission in the multivariate model which controlled for BMI, sex, race, ASA class, presence of comorbidity, urgency of surgery, length of stay, and discharge destination. Patients were most commonly readmitted for organ/space SSI, sepsis, pneumonia, and deep incisional SSI – complications related to infection. “Non-clean” wound class was also associated with significantly increased risk of these postoperative infectious complications in multivariate models.Interestingly, “dirty/infected” wound class did not have a higher readmission risk than “clean/contaminated” or “contaminated” surgeries (when compared to “clean”). This may be a result of less physiologically complicated “dirty/infected” surgeries, as they were mostly represented by colectomies (Supplementary Table 2). Alternatively, it could be due to greater anticipation of postoperative infection when the surgical field has been grossly contaminated. Therefore, these patients may have had prolonged postoperative antibiotic which decreased readmission risk.Wound classification had not previously been investigated as an important factor to consider for readmission risk among a broad group of surgeries. The wound classes were originally developed as a tool to estimate risk for surgical site infections and has been categorized as a "Must” document element at time of surgery by the CDC since 1985 . In a stReadmission after surgery is an important outcome to investigate because CMS expanded its Hospital Readmission Reduction Program (HRRP) to include unplanned readmissions after total hip replacement and total knee replacements in 2015 and CABG in 2017 .The most significant finding of this study was the utility of wound class in prognosticating risk of 30-day readmissions following surgery. Patients undergoing surgeries with “non-clean” wound class may be at a statistically significant, greater than two-fold, risk of readmission when compared to “clean” surgeries Fig. . If expaFindings from Table This study has several limitations. The multivariate model of readmission does not take into account the hospital course (except for adjusting for length of stay and discharge destination) nor does it account for the indication for surgery (except for its urgency). These factors may confound the association between wound classification and readmission. Future studies should account for surgical indication and postoperative antibiotic use or surgical management. Secondly, inconsistencies in the reported wound classification in the ACS NSQIP database have been documented . AlthougWound classification was strongly prognostic of 30-day readmission in multivariate models, suggesting that it may serve as a marker of readmissions after surgery. Increased risk of postoperative infectious complications associated with dirtier wound classifications may have contributed to this elevation in readmission risk. Since readmission following surgical procedures is commonly viewed as a quality-of-care measure and can result in penalties for hospitals, it is important to recognize that readmissions following a surgery cannot be viewed uniformly. Wound classification is one characteristic which highlights why a standardized readmission rate may not be an appropriate metric to evaluate the quality of surgical care.Supplementary file1 (HTML 1 KB)Supplementary file2 (XML 97 KB) Below is the link to the electronic supplementary material.


During the pandemic, health professionals had the unprecedented experience of dealing with a newdisease with high contagiousness and mortality. The workload, but also the fear of disease management, caused significant levels of stress. Each employee managed stress in his own way. This study detects the level of stress experienced by health workers during the pandemic and the coping strategies they selected accordingly.The aim of this study was to investigate the coping strategies used by health professionals during the pandemic of coronavirus.The sample comprised 180 health professionals that were working in a public hospital. The data collection tool was an anonymous questionnaire consisting of socio-demographic questions, the Toulouse Scale for coping , and a sub questionnaire to explore health professionals’ views on the pandemic.Women (73.9%), young individuals (50.6% are up to 35 years old) and TEI graduates (53.9%) predominate in the sample. The sample consisted mainly of nurses (68.3%) and the great majority were contract workers (67%).The most frequently used dimensions were “Active focus” (Average 3.91/5.00), “Acceptance” (3.86), “Cognitive Control and Planning” (3.61) and “Social Information Support” (3.60).Also, health workers used more often the strategies of “Social support” (3.45), “Control” (3.33) and “Focus” (3.23), while they use the “Withdrawal” strategy less often (2.25). Finally, respondents used “Positive strategies” more often (3.54) than negative ones.The health professionals in the present study preferred to ask for information and use cognitive and informative strategies more often and, to a lesser extent, they were overwhelmed by their emotions. The strategies of social support, control and focus were used more often, while the strategy of withdrawal was selected less often. Furthermore, positive strategies were used to a greater extent.None Declared


Benefiting from this tuning strategy, the optimized electrocatalytic anodic membrane successfully achieves >99% pollutant (propranolol) degradation during a flow-through (~2.5 s for contact time), high-flux (424.5 L m−2 h−1), and long-term (>720 min) electrocatalytic filtration test at a very low energy consumption (0.029 ± 0.010 kWh m−3 order−1). Our findings highlight a controllable preparation approach of catalysts while also elucidating the molecular level mechanisms involved.Achieving controllable fine-tuning of defects in catalysts at the atomic level has become a zealous pursuit in catalysis-related fields. However, the generation of defects is quite random, and their flexible manipulation lacks theoretical basis. Herein, we present a facile and highly controllable thermal tuning strategy that enables fine control of nanodefects via subtle manipulation of atomic/lattice arrangements in electrocatalysts. Such thermal tuning endows common carbon materials with record high efficiency in electrocatalytic degradation of pollutants. Systematic characterization and calculations demonstrate that an optimal thermal tuning can bring about enhanced electrocatalytic efficiency by manipulating the N-centered annulation–volatilization reactions and C-based  Achieving controllable fine-tuning of defects is desired for electrocatalysts. Here, the authors present a facile and controllable thermal tuning strategy that enables fine control of nanodefects, thereby endowing common carbon materials with high electrocatalytic oxidation efficiency. A central topic attracting extensive attention in these areas is the development of robust and highly efficient catalysts5, which is also the bottleneck that hinders their future advancement and applications. To date, a large number of studies have brought about a variety of electrocatalysts8, reaching a consensus that the elemental composition and lattice structure of the catalysts have a profound impact on their electrocatalytic performance11. Correspondingly, achieving controllable fine-tuning of catalysts at the atomic level has become a zealous pursuit14. However, such fine-tuning or fabrication methods are rarely reported, and the few reported methods are relatively complex and based on scarce and costly materials16, rendering them undesirable for scaled-up applications.Progress in electrocatalytic science in the past decade has promoted the development of numerous related research areas, including H17, especially for carbon-based catalysts19. In general, defects in carbon-based catalysts are caused by the doping of extrinsic substances 18 or by intrinsic mechanisms which lead to abnormal sites  in conjugated networks20. The resulting defects can cause significant alteration of electron-density distribution, thereby potentially enhancing electron transfer and electron exchange with exogenous substances and thus forming more reactive catalytic centers17. Therefore, an effective approach to fine-tuning catalytic performance can be developed through manipulating the formation of structural defects. While the occurrence of defects seems to be random and uncontrollable20, the lack of theoretical guidance on defect-generation mechanisms hinders the development of fine-tuning methods.Recent studies have revealed the fundamental importance of creating structural defects in improving the electrocatalytic performance of catalysts23. After a simple carbonization treatment, the obtained carbon-nanofiber (CN)-based membranes were systematically characterized through X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, and electron spin resonance (ESR), in order to reveal their characteristics in terms of atomic/lattice arrangements. Quantum chemical calculations based on density functional theory (DFT) and multiphysics simulations were performed to further reveal the thermal tuning mechanism. It was demonstrated that fine-tuning of structural defects can be achieved through a simple alteration of the carbonization temperature, thereby conspicuously affecting electrocatalytic performance. Systematic experiments were performed to investigate the detailed electrocatalytic behavior of the CN membranes and the corresponding reaction mechanisms were elucidated. We envision this facile and controllable thermal tuning strategy will lay the foundation for the precise preparation of nitrogen-doped carbon-based catalysts and fine manipulation of their catalytic performance.Herein, we propose a thermal tuning strategy that enables fine-tuning of structural defects via subtle manipulation of atomic/lattice arrangements based on a facile and highly controllable carbonization process. Nitrogen-containing polyacrylonitrile (PAN) was employed as a raw material and was electrospun into a porous membrane composed of highly connected nanofibers. Such a nanofiber-based membrane framework has been increasingly recognized as an efficient structure for catalytic processes, owing to its high specific surface area, superior mechanical properties, and versatile application formsAn electrospinning–thermal-tuning strategy Fig.  was empl27  could further promote the linkage of the polypyridine clusters due to enhanced carbonization effect, while creating defects by burning away some N atoms  can transform the raw PAN into dispersed clusters of locally sintered polypyridine structures27 Fig. . A certaoms Fig. . Hence,  29 Fig. . Such hiIn general, the prepared free-standing CN membranes  can be used to assess the relative content of the lattice-disordered carbon in the CNs. The calculated AC−sp3/AC−sp2 first increased from 0.83  and annular bright field scanning transmission electron microscopy (ABF-STEM)32 using the focused ion beam-scanning electron microscope (FIB-SEM) technique to prepare the samples . A gradual decrease of the determined FWHM was observed within the 700–1500 °C range. This observation indicates that a higher thermal treatment temperature would lead to a higher degree of crystallization, which is positively correlated with the degree of graphitization.The change in the crystal structure of the CNs uncovered by X-ray diffraction  and thus potentially increased catalytic activity33 of CN_900  associated with the disordered defective C-sp3 configuration and G bands (~1580 cm−1) associated with the ordered graphitic C-sp2 configuration10 were observed for all the CNs. The lattice disorder degree of a carbon material can be evaluated by the ratio of the D band area to the G band area (AD/AG)20. As presented in Fig. AD/AG first increased from 2.91 (CN_500) to 3.56 (CN_900), but then dropped to 1.61 (CN_1500). The variation of the degree of lattice disorder suggested by these results was highly consistent with that revealed by the XPS results  data  can generate defects, primarily via the N-centered annulation–volatilization reactionsPAN Fig.  into parPAN Fig. . Then, v20. Correspondingly, the calculated HOMO − LUMO gaps followed the order of CN_500 > CN_900 > CN_1500, indicating that electron transfer became faster as the treatment temperature increased. These results corresponded well with the measured conductivities  did not give rise to a higher removal rate , but the effect on electrocatalytic reaction enhancement becomes insignificant in the higher range . The results further imply that material conductivity might not be the central pursuit in developing electrocatalysts.The effect of CN conductivity on its electrocatalytic performance was first evaluated through multiphysics simulations. The established multiphysics model Fig.  simulateate Fig. . These r40. In general, the CN_500 and CN_1500 which possessed minor defects were basically negatively charged, except around the carbon matrix and H-edged regions  of a propranolol (PRO) molecule enriched on the CN_500, CN_900, and CN_1500 unit-regions were calculated to be −0.63, −0.896, and −0.80 eV, respectively  from the CN_500, CN_900, and CN_1500 to PRO were calculated to be 0.09, 0.17, and 0.12 e, respectively. Overall, CN_900 was expected to achieve the highest electrocatalytic efficiency due to its advantageous defective atomic/lattice arrangements.Additional DFT calculations were performed to support the thermal-tuning mechanism and to predict the impact on electrocatalytic activity. The negative and positive regions in the electrostatic potential (ESP) diagram Fig.  represenons Fig. . In markons Fig. , suggestely Fig. . A highe22. A great number of studies reported the benefits of fabricating or incorporating electrocatalysts into membrane-like structures, which could be readily used as membrane electrodes in various applications44. Among them, electrocatalytic membrane filtration (EMF) exhibits great potential in tackling the global water crisis, mainly owing to its multifaceted advantages originating from the combination of membrane separation and electrocatalysis. For example, electrocatalytic reactions could be promoted during filtration, owing to abundant active sites due to high porosity, enhanced mass transfer22 and confinement effect by membrane nano-channels45. In addition, electrocatalysis could also contribute to the mitigation of membrane fouling46.The porous structure (porosity of 63.1%) of the electrospun CN membrane allows flow-through configurations, which could facilitate contact with target reactants, increasing reaction opportunities−1) as a model pollutant. As shown in Supplementary Fig. k, min−1, based on Supplementary Fig. We employed the CN membrane as an anode in a batch-mode EMF system  was not necessarily enhanced with the increase of doped N amount. As shown in Supplementary Fig. 48 proposed that the improvement of catalytic activity by N doping could be mainly attributed to the formed pyridinic N and graphitic N  and AC−sp3/AC−sp2 ratio (as an index of the relative lattice defective carbon content) also supported this conclusion  in nitrogen-doped carbon materials when excited by external energy 52. Therefore, we surmise that the lattice defects are basically oxidative holes (h+)53. Accordingly, we performed a series of quenching tests in a batch mode 54 to quantify the contribution of h+ to PRO degradation. The obtained results 55) was used to investigate the possible contribution of 1O2, which could be generated by the energy conversion or charge transfer during the separation and recombination of electron–hole pairs57. The ESR spectra also clearly showed the typical signals of TEMP-captured 1O2  solvent environment is generally ascribed to the 1O2-specific kinetic solvent isotope effect58  oxidation was minimal , ciprofloxacin (CIP), cimetidine (CMT), and sulfamethoxazole (SMX) were also separately used as representatives of pharmaceuticals and personal care products in the continuous-mode EMF tests. As presented in Fig. k of 114 min−1) with very low energy consumption (0.029 ± 0.010 kWh m−3 order−1 of EEO) . Remarkably, no apparent decrease of PRO removal rate was observed. This observation indicated that the membrane was able to continuously degrade >99% of PRO during the flow-through process (~2.5 s contact time). Also, the chemical compositions and morphology of the CN_900 remain stable after the long-term tests ), the CN membrane showed a remarkably high electrocatalytic efficiency , but also changes them back to an ordered graphitic C-sp2 configuration at high temperatures . Consequently, the catalytic performance can be controlled based on the fine-manipulation of atomic/lattice arrangements.We have shown that the proposed thermal tuning strategy enabled fine-tuning of the atomic/lattice arrangements of both the N element and C matrix. Through simply altering the carbonization temperature, we can control the transformation of pyrrolic N to graphitic N, and at the same time create defects by burning off a portion of N. Simultaneously, alteration of the carbonization temperature would also affect the lattice state of the C matrix. Increasing of temperature first increases the proportion of disordered defective C-Quantum chemical calculations based on DFT further verified the mechanism of this thermal-tuning strategy. It was demonstrated that an optimal thermal treatment could bring about enhanced redox activity, increased electron transfer capacity, and improved adsorption capability, thus dramatically boosting electrocatalytic efficiency. In combination with multiphysics simulations, the importance of increasing electrocatalytic active sites rather than maximizing conductivity was emphasized.−2 h−1), and long-term (>720 min) test, at a very low energy consumption (0.029 ± 0.010 kWh m−3 order−1 of EEO), suggesting a great potential for sustainable applications. We envision that this thermal tuning strategy will lay a scientific foundation for future precise and controllable fabrication of various types of catalysts.Benefiting from this tuning strategy, the optimized CN_900 membrane achieved a high electrocatalytic degradation efficiency. It successfully maintained a > 99% degradation rate during a flow-through (~2.5 s contact time), high-flux  in N,N-dimethylformamide  was prepared under vigorous stirring. Then, the solution was introduced to a laboratory electrospinning system , where the PAN solution was driven by an applied electric field as nanofibers to a rotating (2500 rpm) cylinder collector. The tip voltage, collector voltage, inner needle diameter, tip-to-collector distance, and injection speed during electrospinning were set to 15 kV, −7.5 kV, 0.5 mm, 15 cm, and 1.5 mL h−1, respectively. After 10 h of electrospinning at 40% ± 10% humidity and 25 ± 5 °C, the obtained PAN nanofiber mat was detached for the subsequent thermal tuning treatment. For thermal tuning, the nanofiber mat was first pre-oxidized in air at 280 °C for 2 h with a heating rate of 2 °C min−1, and then subjected to a higher temperature  heating treatment in Ar for 1 h with a heating rate of 5 °C min−1. This thermal tuning treatment was intended to create defects via carbonization and maximizing the defect activity via shaping atomic and lattice arrangements. After cooling, the resulting membranes were used for characterization and designated as CN_500, CN_700, CN_800, CN_900, CN_1100, CN_1300, and CN_1500 membranes, respectively. Furthermore, when the carbonization operation for CN_900 was performed in an air atmosphere with O2, the obtained material would not have a defective structure . Membrane conductivity was assessed with a four-probe tester .Membrane morphology was observed through SEM  and TEM . Elemental distribution and chemical composition of the membrane were determined through TEM mapping and XPS , respectively. Membrane crystal structure was investigated with XRD . Membrane molecular structure was evaluated with a Raman spectrometer  at 532 nm. An X-band ESR spectrometer  was employed to reveal the defective structures. The peak-to-peak width of the singlet in the obtained ESR spectra was recorded as Δ−4 Ha/Å for the total energy, <0.02 Ha/Å for the force on each atom, 0.05 Å for the tolerance deviation, and 5.0 × 10−3 Ha/atom for the self-consistent accuracy. Then the ESP diagram was drawn based on the calculation results to analyze the relative electron density in the molecules. All calculations were performed under ground-state geometric conditions. The results of EDD were achieved by Miliken Population Analysis. The electron transfer number (Q) was obtained from the EDD results. The adsorption energy (ΔEad) was calculated fromEtot, Ecm, and Eads indicated the total energy of the adsorption matrix, CN membrane, and adsorbates, respectively.The Gaussian 16 C. 01 software was employed for all the model constructions and calculations. The ideal molecular structures of the representative materials  were first constructed with their edges terminated by H atoms to maintain electroneutrality. Optimizations of the molecular structures were performed using the IEFPCM base model with the B3LYP functional and 6-311 G  basis set. The calculation accuracy can be improved by correcting the weak interaction with the DFT-D3 method. For all the geometric relaxations, the selected convergence indicators were 1.0 × 10−5, 0.001, 0.2, 3.2, 18.2, 31.5, and 36.4 S cm−1, which were determined according to the actual conductivity measurements of the CN_500, CN_700, CN_800, CN_900, CN_1100, and CN_1500 membranes, respectively. In the simulations, the distributions of normalized pollutant concentration (C/C0) within the 3-D domain were calculated. The distributions of electric current density and other electric parameters were solved by the Poisson equation62. The advection and diffusion of pollutant were described by combining the Navier-Stokes and Nernst-Planck equations63. The boundary condition and related coefficients64 were listed in Supplementary Table The microscopic model of a CN Fig.  was esta2. A synthetic solution containing PRO  and 10 mM Na2SO4 (as electrolyte) was used as feed. During the test, a 3 V voltage was applied to the EMF reactor to initiate electrocatalytic reactions. For different research purposes, the EMF system can be operated in either a batch mode . The membrane to be tested and a titanium mesh  were installed in the EMF reactor as the anode and cathode, respectively. The effective filtration area was 7 cmC) were determined with a high-performance liquid chromatograph (HPLC) instrument (Agilent 1200) equipped with a C-18 column (Agilent Eclipse XDB-C18). A mixture containing 7:3 (v/v) 0.1% phosphoric acid solution/acetonitrile was used as the eluent. The injection volume was set to 5 μL when the feed PRO concentration was 20 mg L−1, and set to 50 μL when that was 0.5 mg L−1. The flow rate, column temperature, and detection wavelength were set to 1.0 mL min−1, 30 °C, and 218 nm, respectively. The chemical composition of the effluent was further analyzed with an HPLC‒MS/MS  instrument22. The luminescent bacteria Vibrio fischeri was used for the acute toxicity analysis (ISO 11348-3 as reference)65. The acute toxicity test relies on the inhibition of cell activity to decrease the respiratory rate, which further leads to the corresponding decrease of bacterial luminescence.PRO concentrations  and C (mg L−1) are the initial and timely measured  PRO concentrations, respectively. The EEO (kWh m−3·order−1), as an energy consumption index, was calculated as:U (V) is the applied voltage, I (A) is the measured current, t (min) is the reaction time, and V (m3) is the total influent volume involved in the reactions.The rate constant +, 1O2) and quantify their contributions to the PRO degradation during the EMF tests. For the quenching tests, a 4 mM portion of EDTA-2Na  and a 16 mM portion of FFA  were separately added to the feed solution  to quench the possibly generated h+ and 1O2, respectively. For the ESR tests, a 100 mM portion of TEMP 66 was added as the trapping agent of 1O2 to a 10 mM Na2SO4 solution, which was then used as the feed for the EMF experiment. The effluent was analyzed using an X-band ESR spectrometer . The adopted recording parameters were 1 mW for microwave power, 15 mT for sweep width, 0.1 mT for modulation amplitude, and 120 s for sweep time. Probe tests were conducted to identify the presence of ·OH7. A trace amount of TA (100 μM) was added to a 10-mM Na2SO4 solution to capture ·OH, which was then changed to hTA.A series of quenching, ESR and probe tests were conducted to identify the presence of multiple reactive species (e.g., hSupplementary Information


To estimate the pooled prevalence of sexual dysfunction (SD) in women with multiple sclerosis (MS).We systematically searched PubMed, Scopus, EMBASE, Web of Science, and google scholar and also gray literature up to October 2021.The search strategy includes:(“Multiple Sclerosis” OR “MS” OR “Disseminated Sclerosis” OR (Disseminated AND Sclerosis) OR (Sclerosis AND Multiple)) AND  OR  OR  OR  OR “Sexual Dysfunctions” OR “Sexual Disorders” OR “Sexual Disorder” OR “Psychosexual Dysfunctions” OR  OR  OR “Psychosexual Dysfunction” OR “Psychosexual Disorders” OR  OR  OR “Psychosexual Disorder” OR “Hypoactive Sexual Desire Disorder” OR “Sexual Aversion Disorder” OR  OR  OR “Sexual Aversion Disorders” OR “Orgasmic Disorder” OR (Disorders AND Orgasmic) OR “Orgasmic Disorders” OR “Sexual Arousal Disorder” OR  OR  OR “Sexual Arousal Disorders” OR “Frigidity”).2:95.7%, P < 0.001). The pooled prevalence of Anorgasmia in MS patients estimated as 29% (95%CI:20–39%) . The pooled odds of developing SD in MS women estimated as 3.05(95%CI: 1.74–5.35) . The pooled prevalence of decreased vaginal lubrication in MS patients estimated as 32%(95%CI:27–37%) . The pooled prevalence of reduced libido was 48%(95%CI:36–61%) . The pooled prevalence of arousal problems was 40%(95%CI: 26–54%) . The pooled prevalence of intercourse satisfaction was 27% (95%CI: 8–46%) .We found 2150 articles by literature search, after deleting duplicates 1760 remained. Fifty-six articles remained for meta-analysis. The pooled prevalence of SD in MS patients estimated as 61% (95%CI:56–67%) (IThe result of this systematic review and meta-analysis show that the pooled prevalence of SD in women with MS is 61% and the odds of developing SD in comparison with controls is 3.05. Multiple sclerosis (MS) is a degenerative, neurologic disease of the central nervous system (CNS) affecting women more than men . It usuaPrimary SD is the consequence of neurological changes in the body, while secondary SD is due to MS-related complications such as fatigue, pain, spasticity, bladder, and bowel dysfunction . TertiarLoss of orgasm, libido, lubrication, and increased spasticity are common during sexual activity in women with MS  , 12.Different factors such as age, disease duration, disability level, bladder dysfunction, cognitive impairment, and disease course influence SD in MS women –15.Up to now, different original studies have been conducted and three previous systematic reviews and meta-analyses estimated the pooled prevalence of SD in women with MS –18. The Cross-sectional studies, Articles that had been published in the English language.Case-report, RCT studies.We followed The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 for reporting this systematic review .Two independent researchers systematically searched PubMed, Scopus, EMBASE, Web of Science, and google scholar and also gray literature up to October 2021.(“Multiple Sclerosis” OR “MS” OR “Disseminated Sclerosis” OR (Disseminated AND Sclerosis) OR (Sclerosis AND Multiple)) AND  OR  OR  OR  OR “Sexual Dysfunctions” OR “Sexual Disorders” OR “Sexual Disorder” OR “Psychosexual Dysfunctions” OR  OR  OR “Psychosexual Dysfunction” OR “Psychosexual Disorders” OR  OR  OR “Psychosexual Disorder” OR “Hypoactive Sexual Desire Disorder” OR “Sexual Aversion Disorder” OR  OR  OR “Sexual Aversion Disorders” OR “Orgasmic Disorder” OR (Disorders AND Orgasmic) OR “Orgasmic Disorders” OR “Sexual Arousal Disorder” OR  OR  OR “Sexual Arousal Disorders” OR “Frigidity”).After obtaining the results, and importing them to Endnote, they omitted duplicates. Then titles, and abstracts were screening, and potential full texts were obtained. The researchers extracted data from each study, entered in Excel, and in the case of discrepancies, the third researcher solved the problem.Data regarding first author, country of origin, number of enrolled patients, number of cases with SD, mean age, mean EDSS, mean duration of the disease, were collected.2) was calculated.All statistical analyses were performed using STATA . To determine heterogeneity, Inconsistency  of developing sexual dysfunction in women with MS comparing to healthy controls were calculated.We found 2150 articles by literature search, after deleting duplicates 1760 remained. Fifty-six articles remained for meta-analysis               , we also found that the pooled prevalence of intercourse satisfaction was 27%.We included all studies which applied different diagnostic tools, so out estimate would be higher than previous ones.In a previous systematic review and meta-analysis which was conducted by Zhao et al., the relative risk (RR) of developing SD in MS women was 1.87 which shows that women with MS have 87% increased risk of developing SD . They alIn our previous systematic review, which we included only studies that applied FSFI questionnaire for evaluating SD in MS, the pooled prevalence of SD estimated as 55% .In 2008, Tzorts et al. evaluated 63 women with MS using FSFI questionnaire, and reported SD in22 and reported no depression in affected cases .Zorzon et al. used Szasz Sexual Functioning Scale for SD assessment and reported SD in 44 out of 70 cases. Anorgasmia or hyporgasmia followed by decreased vaginal lubrication were the most affected subscales .The variation about the prevalence of SD in included studies is due to unclear definition of SD, diverse inclusion, and exclusion criteria, various diagnostic methods, no standardized tools, and cultural issues.SD is an important issue in marital life which is ignored by most physicians and patients. It is a multi-dimensional issue which affects quality of life as well as psychological well-being. Different factors such as disease duration, disability level, psychological disorders such as depression, anxiety, and stress are considered to play role in SD development in MS while there is controversies between studies .Most physicians do not pay attention to this part of their patient’s lives, and patients hesitate to talk about intimate issues.Depression is negatively correlated with FSFI score and its subscales in a previous original study . On the In another study, higher age was associated with increased SD prevalence in MS  while ÇeAnother influencing factor is bladder dysfunction in MS cases which negatively affects their sexual activity . FragalaThe association refers to S2, S3 and S4 innervation of bladder, which control sexual response . On the Zivadinov et al. investigated that physical disorders, depression, age at MS onset, and also neurological impairment while they reported no correlation between SD and duration of the disease .Higher disability level, depression and anxiety were related with SD presence in Demirkian et al. study .This systematic review has some strength. First, we included all studies which evaluated SD. Second, the number of included studies is high. Third, we analyzed all subscales of SD.We also had some limitations. First, all included studies used various diagnostic tools. Second, there was no clear definition of SD. Third, inclusion criteria of participants differed between studies.The result of this systematic review and meta-analysis show that the pooled prevalence of SD in women with MS is 61% and the odds of developing SD in comparison with controls is 3.05.