dataesr/scientific-paragraphs-categorization huggingface dataset:
Prevention Fig. 2  Multivariable analysis of factors associated with the first hospitalised incisional hernia. Volume of activity of the hospital for the incisional hernia hospital stay (the annual number of abdominal surgeries of interest performed in that hospital, in quartiles). HR hazard ratio; LL lower limit of the 95% confidence internal; UP upper limit of the 95% confidence interval; COPD chronic obstructive pulmonary disease. Undergoing a laparoscopy or chronic kidney disease was not associated with incisional hernia repair (not shown on the figure) of incisional hernia is hence particularly warranted, including by considering incisional hernia and hernia recurrence risk factors. In France, an earlier study on incisional hernia repair on the same database but restricted to patients who underwent a laparotomy (including for cardiovascular procedures) found that 5% of patients had an incisional hernia repair within years of the laparotomy  [1].
The main individual risk factors for incisional hernia repair were diabetes, high blood pressure, and, consistent with our findings, COPD, obesity, and older age (as early as 39 years old). As more surgical procedures are performed using the laparoscopic technique  [12] , the incidence of hernia repair should decrease  [4]. In 2011, in a study on 3239 patients in a sample of 51 French hospital estimated the hospital-related incisional ventral hernia repair to be €4731  [13] , close to our finding of €4153. This study further assessed the mean indirect cost (sick leave) of incisional hernia repair in working patients to be €5376. This study has several strengths. First, it is based on real-life data, on an exhaustive database offering national coverage. The large study population offered to perform sub-group analyses (by field of surgery, by age group, etc.
Due to the absence of clinical results in the claims database, even examining doctors' visits in the community setting (in the existing extension of the PMSI) would not inform on the possible occurrence of an incisional hernia not leading to a hospitalization. Second, due to legal restrictions in the historic analysis of PMSI database (maximum of 9 years prior to current year) and the need to check for medical history during at least 2 years prior to index surgery, we only included patients with an incisional hernia repair within 5 years of the index surgery. Thus, patients presenting an incisional hernia repair beyond 5 years were not captured and this also leads to an underestimation of the hernia recurrence rate. However, three quarters of incisional hernias are diagnosed (but not necessarily repaired) within the first 2 years after initial laparotomy  [5].
Third, data from primary care are not available in the PMSI; hence this study does not report incisional hernia care consumption without hospital surgical repair. Hence, this study underestimates the incisional hernia's public health burden compared to its real life healthcare consumption. Fourth, patients who died before the incisional hernia repair are not included in the study. This introduces a bias for pancreatic cancer patients, who have a high mortality rate. Fifth, it is not possible to totally ascertain the causal relationship between the index surgery and the incisional hernia repair. Yet, to reduce bias and strengthen the potential likelihood of a correlation, we excluded patients with multiple abdominal surgeries. This precaution strengthens the likelihood of a causal relationship between the repaired incisional hernia and the index surgery.
Taken together, these conclusions suggest that our study results could still apply to the majority of abdominal surgeries after the study period. In addition, some limitations are related to the database. The absence of clinical details on the hernia and the abdominal surgery (such as incision length and surgery duration) prevents the examination of these crucial risk factors  [4, 26]. Moreover, lifestyle risk factors and morphometric features are not collected in this claims database (obesity was limited to the collection of the ICD-10 code on the hospital discharge form and was certainly underreported if it had limited impact on the hospital stay). Consequently, the study design was not suitable for studying the impact of known factors (e. g. , smoking  [7] , certain morphometric domains  [27] , or closure technique of the index surgery), other risk factors, or the physiopathology of incisional hernia.
As a study based on a claims database, the quality of the results depends on the quality of the coding of the hernia repair performed at the hospital. In France, hospitals are reimbursed by the NHI for procedures they perform on patients and care they provide. More than 85% of study patients had a medical procedure (CCAM code) of interest and 59% of patients had an ICD-10 code for incisional hernia (47% as PD or RD and 12% as SAD). Overall, the incisional hernia was identified through both a CCAM code and an ICD-10 code in the database in the majority of the study patients, insuring a high level of trust in the validity of the inclusion of the patients.
Conclusion In this nationwide population, using an exhaustive healthcare database, at least 4. 6% of patients who underwent an abdominal surgical procedure over 2013-2014 were admitted to the hospital for an incisional hernia repair within 5 years, and among them the recurrence rate was 15. 6%. The hospital care cost of incisional hernia repair for patients undergoing an abdominal surgery was estimated to be about €4150 per hernia repair. The field of surgery with the highest risk of subsequent incisional hernia repair was colon and rectum. Also, the machine learning analysis showed that individual risk factors (age starting as early as age 39, or surgery with laparotomy) also put patients operated on other fields of surgery at higher risk of incisional hernia repair. In clinical practice, identifying high-risk patients and applying specific measures and technologies to prevent the onset of incisional hernia is warranted.
Acknowledgements We thank the  ATIH (Agence Technique de l'Information sur l'Hospitalisation)  for the data extraction. We thank  Julien Beisel  ( ORCID 0000-0002-7326-6745 ) for the machine learning analysis,  Baptiste Jouaneton  ( ORCID 0000-0001-6764-4335 ) for data management supervision,  Gwendoline Poinsot  ( 0000-0002-5493-3506 ) for the statistical analyses, and  Joannie Lortet-Tieulent  ( ORCID 0000-0002-8816-7475 ) for writing and editing the manuscript. All work for Heva, the company contracted by  Becton, Dickinson and Company  to carry out the study.
Data availability The data supporting the study findings are part of the SNDS and are available from the HDH (Health Data Hub https:// www. health-data-hub. fr/). Restrictions apply to the availability of these data. Permission to access these data was granted by the French data protection authority (Comité National de l'Informatique et des Libertés, CNIL). The study was registered under MR006 with the Health Data Hub on 21/04/2021 (Declaration of conformity No. 2214421v0 of 17/07/2019).
1b ; signed-rank test, S+ vs Ref: z = -6. 5, p < 0. 001; V+ vs Ref: z = -9. 5, p < 0. 001). Participants also switched more between choice options in these two conditions (S+ vs Ref: z = 3. 8, p < 0. 001; V+ vs Ref: z = 7. 6, p < 0. 001), and particularly so in the volatile condition (V+ vs S+: z = 5. 1, p < 0. 001). These differences between conditions were replicated in the second dataset (Supplementary Fig. 1a ).
Dimensional analysis of mental health symptoms To study the relation between compulsivity and task behavior, we also administered a battery of nine self-report questionnaires assessing a large range of mental health symptoms known to fluctuate across the general population  6, 8  (first dataset: Fig. 3 ; second dataset: Supplementary Fig. 4 ). Participants' mental health profiles varied within and across questionnaires, with most participants scoring within the normal range of symptoms (Fig. 3a ). Due to the strong correlations between questionnaire-specific scores, we employed a previously validated dimensional approach to the analysis of item-specific symptom scores  8. Using a factor analysis (see Methods), we extracted three symptom dimensions from the covariance structure of responses to the 209 questionnaire items (Fig.
Associations between symptom dimensions and decision variability We first regressed accuracy and switch rate, averaged across task conditions, against the three symptom dimensions and control variables (Fig. 4a ; see Methods). We found that accuracy was associated negatively with CIT (β = -0. 255 ± 0. 010, p = 0. 011) and positively with ICAR-16 (β = 0. 205 ± 0. 088, p = 0. 021). By contrast, switch rate was associated positively with CIT (β = 0. 235 ± 0. 099, p = 0. 019) and negatively with ICAR-16 (β = -0. 243 ± 0. 087, p = 0. 006). These results mean that CIT correlates negatively with accuracy (rank correlation, ρ = -0. 230, p = 0. 007) and positively with switch rate (ρ = 0. 220, p = 0. 010). These effects of CIT were replicated in the second dataset (Fig. 4b ; accuracy: replication Bayes Factor (BF) = 9.
We then regressed learning noise and choice temperature against the three symptom dimensions and control variables (Fig. 5a ). We found no association between learning noise and any symptom dimensions, but a positive association between choice temperature and CIT (β = 0. 291 ± 0. 096, p = 0. 003) and a negative association between choice temperature and ICAR-16 (β = -0. 202 ± 0. 085, p = 0. 019). This result means that CIT correlates positively with choice temperature (ρ = 0. 276, p = 0. 001), but not with learning noise (ρ = 0. 108, p = 0. 208). This selective association between CIT and choice temperature was replicated in the second dataset (Fig. 5b ; replication BF = 9. 8). As a control analysis, we regressed accuracy, switch rate and choice temperature against each of the nine questionnaire-specific scores (Supplementary Fig.
In line with account #2, we found that CIT correlated positively with choice temperature in both conditions (Fig. 5a ; V+: ρ = 0. 208, p = 0. 015; S+: ρ = 0. 293, p < 0. 001). This pattern confers decisive evidence for a maladaptive account of this association (BFV0 = 258. 0; see Methods). This maladaptive association between CIT and choice temperature in the stochastic condition was replicated in the second dataset (Fig. 5b ; BFV0 = 31. 9). Although the positive association between CIT and choice temperature is maladaptive, we found no association between CIT and the magnitude of changes in learning rate, learning noise and choice temperature in the volatile condition (learning rate: p = 0. 916, BF1/0 = 0. 184; learning noise: p = 0. 481, BF1/0 = 0. 230; choice temperature: p = 0. 748, BF1/0 = 0.
To unpack individual differences in choice temperature into quantifiable trait and state components, we conducted a test-retest analysis in a subgroup of participants (N = 100 from the first dataset; see Methods). Accuracy, switch rate, learning noise and choice temperature all showed fair-togood reliability (Fig. 6a ; accuracy: intra-class correlation (ICC) = 0. 441, p < 0. 001; switch rate: ICC = 0. 709, p < 0. 001; learning noise: ICC = 0. 468, p < 0. 001; choice temperature: ICC = 0. 718, p < 0. 001). These results mean that the trait (i. e. , test-retest reliable) component of learning noise explains about a fourth (22. 2%) of its observed variance. In contrast, the trait component of choice temperature explains about half (52. 0%) of its observed variance (Fig. 6b ). Assuming that CIT reflects trait compulsivity and can therefore only explain the trait component of choice temperature, this means that CIT explains 8.
Statistical analyses We used Wilcoxon signed-rank tests for comparing (i) model-free variables, accuracy and switch rate; and (ii) best-fitting model parameters between conditions. We employed non-parametric Spearman correlations unless specified otherwise. We computed standard errors on Spearman's rank correlation coefficients (Fig. 5 ) using bootstrapping. We randomly sampled datasets from the original dataset (with identical sample size and replacement) 1,000 times and calculated the correlation on these new datasets. To investigate the associations between mental health symptoms and task behavior, we conducted regression analyses (Fig. 4  and 5 ). We specified accuracy, switch rate, and model parameters in turn as dependent variables, in separate regressions. Predictors were scores on the three symptom factors, alongside age, gender and cognitive ability (ICAR) as additional control variables.
Statistical significance was assessed using one-sample t-tests on regression coefficients. To directly illustrate the associations between task variables and symptom scores identified, we also examined Spearman correlations between CIT and accuracy, switch rate (Fig. 4 ), learning noise, and choice temperature (Fig. 5 ). To examine whether the identified associations replicated in our second dataset, we computed Bayes Factor (BF) tests for replication success  42  answering the question "Is the effect from the replication attempt comparable to what was found before, or is it absent?". To examine the maladaptation of choice temperature in the stochastic condition (S+) with stable option values, we computed two Bayes Factors tests on the correlation coefficients between choice temperature and CIT.
First, we calculated the relative evidence of finding a zero correlation in a null distribution over finding it in the distribution of S+ (Fig. 5 ; first dataset: BFS0 = 448. 1; second dataset: BFS0 = 35. 1). We then calculated the relative evidence of finding the true S+ correlation from the distribution of V+ over finding it in a null distribution (Fig. 5 ; first dataset: BFV0 = 258. 0; second dataset: BFV0 = 31. 9). These Bayes Factor tests were obtained by calculating the likelihood ratios of the marginal probability density functions derived from normal distributions generated by values of Fisher z-transformations on Spearman's correlation coefficients. To examine reliability between test and retest, we calculated intraclass correlations (ICC) for accuracy, switch rate, and model parameters measured at test and retest sessions in the same participants (Fig.
Acknowledgements This work was supported by a starting grant from the  European Research Council  ( ERC-StG759341 ) awarded to V. W. , and by a department-wide grant from the  Agence Nationale de la Recherche  ( ANR-17-EURE-0017 ,  EUR FrontCog ). M. R. is the beneficiary of a postdoctoral fellowship from the  AXA Research Fund , and is supported by  La Fondation des Treilles. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
1 ), it is the N and P fractions of cetacean wastes that are likely to scale with ecosystem response. On the other hand, N Fig. 5  | Relative nutrient composition of wastes produced by small cetaceans, deep divers and baleen whales. Individual nutrient released per kilogram of food ingested daily was normalized per nutrient and computed for 38 cetacean species, as estimated using a bioenergetic model supplemented with an original dataset of diet composition, prey composition and metabolic data and set up with Monte-Carlo simulations combined with a bootstrap procedure with n = 1 e4. For all nutrients except copper (Cu; with our calculated p-value p = 2. 2 1 e-2 for comparison with small cetaceans) there is no significant difference between the relative composition of each taxon. Boxplots display the median with a solid black line in each box, lower and upper hinges correspond to the 25th and 75th percentile, respectively; upper and lower whiskers extend respectively from the hinges to the largest and lowest values no further than 1.
Variations in the relative composition of released nutrient loads between taxa, areas and habitats could be underestimated here, due to extrapolations made to apply the model to numerous species in numerous areas. Particularly, we assumed a fixed diet per species, and the compositional dataset for prey compiled samples from a unique area (Northeast Atlantic). While we limited the extent of these biases using bootstrapping and functional prey groups to describe diets (see Methods), these parameters should be more precisely and locally set for future replication of our approach at finer spatial scales. This could be especially informative in revealing fine-scale patterns of nutrient deposition. Nutrients released by each taxon also differ in their turnover rates, i. e. the time between the assimilation of nutrients in organic matter and their return to ecosystems in a bioavailable form.
Model uncertainty All parameters are associated with a certain degree of uncertainty and/ or inherent natural variability. To account for it, we combined Monte-Carlo simulations and bootstrapping (1 e4 simulations and drawings, respectively) to simulate vectors of possible values for each parameter based on a given basal value and distribution (Table  3 ). Six model parameters (BM, β, AE, r n , A, t) were sampled from parametric statistical distribution based on published information (Table  3 ). Two (E and x Fe ) were estimated based on the diet of each species and the composition of prey items: uncertainty was quantified using the bootstrap. We found high ranges of variations for some model parameters (Supplementary Data 3), which ultimately impacted uncertainty in the model output.
Excretion of trace nutrients in urine is negligible  45, 66  , so we set nutrient release for the migratory species in breeding grounds to zero for trace nutrients and to between 0. 2 and 0. 4 for N and P (Table  3 ). For population abundance A, we selected only dedicated largescale, multi-species surveys using distance sampling protocols and analysis. When the survey design included several spatial blocks, we used mean estimates and coefficients of variation (CV) of blocks to compute the overall mean and CV for each area (Supplementary Data 4). Distinct estimates between habitats were computed where both oceanic and neritic blocks were surveyed. We then used a lognormal distribution with the calculated parameters for Monte Carlo simulations (Table  3 ). Blue (Balaenoptera musculus), fin (Balaenoptera physalus), Sei (Balaenoptera borealis), Bryde's (Balaenoptera edeni) and humpback (Megaptera novaeangliae) whales are known to migrate from their feeding grounds of temperate and subpolar areas to breeding grounds in tropical to sub-tropical areas  50.
If populations of the same species in different locations are unlikely to feed on the same prey species, they are likely to have the same trophic ecology, and therefore target prey from the same functional groups. We preferred information related to the percentage of biomass ingested of prey species or group of prey species (%W) from stomach content analysis (64 references) rather than qualitative information from isotope analysis, list of prey or surface observations (55 references). When not available, qualitative data was used to describe an average diet. We then compiled energy and nutrient content analytical data of 154 prey samples from two studies  46, 47. We were limited to these data sets in our analysis, but we artificially extended them by using a kernel-based bootstrap procedure to simulate variation in the composition of each prey group and obtain vectors of energy content (E pg ) and nutrient concentration (x n,pg ) per prey group pg.
A distribution for the composition of each prey group (for each nutrient, separately) is estimated from sample values from the compiled data sets and 1 e4 values are then drawn randomly from this estimated distribution. For the prey group Zooplankton, there was only one sample which precluded the use of the bootstrap procedure. We instead conducted Monte-Carlo simulations (n = 1 e4 ) using a normal distribution with the value of the sample as a mean and a standard variation of 20% for energy and macronutrient content and 40% for micronutrient content. Then, we used these energy and nutrient content values (E pg and x n,pg , respectively) and the percentage of prey groups in diets (W pg ) to determine the mean energy content (E, Eq. (  2 )) and the mean iron concentration (x n , Eq. (  3 )): E = X pg W pg × E pg ð2Þ x n = X pg W pg × x n,pg ð3Þ Datasets of prey composition are available under the depository system PANGEA  68, 69  (https://doi.
Acknowledgements This work was supported by the  French Ministry for Ecology ("Ministre de la   Transition Ecologique et Solidaire/Direction de l'Eau et de la Biodiversité -MTES/DEB ") and by the European project  H2020 SUMMER "Sustainable Management of Mesopelagic Resources"  (grant agreement ID:  817806 ). We are grateful to  Nicolas Bousquet  for his guidance in the design of a bootstrap procedure appropriate for our sensitivity analysis.
Data availability No original data were used nor generated for this analysis which mobilised data from the literature for the setting of a majority of parameters in our bioenergetic model. Sources are provided in the manuscript and in Supplementary Data 4. The latter also includes means and coefficient of variations used to define population abundances and the diet description used for each species using functional groups of prey. The external datasets of prey composition are available at https://doi. pangaea. de/10. 1594/PANGAEA. 937345 and https://doi. pangaea. de/10. 1594/PANGAEA. 940861 (see Methods). Oceanographic data was downloaded at https://oceancolor. gsfc. nasa. gov/l3/order/ in March 2022. The download procedure was indicated in a ReadMe file in the corresponding data folder, on the Github repository attached to the study (https://github.
Acknowledgements This study is supported by the  National Key Research and Development Plan  ( 2022YFF0800300 ),  National Natural Science Foundation of China  ( 42173011 ;  41973009 ;  41922019 ;  41625012 ;  41961144028 ;  42293280 ;  42261144668 ;  42073002 ) and  National Science Foundation of United States  (award No. EAR 1760203  to A. D. A). We thank  Benjamin J. W. Mills  and  Jacopo Dal Corso  for kindly providing us with the chronologically calibrated Hg/TOC and Hg isotope data of the Meishan section used in their Hg-C isotope model. Dominik Hülse  is thanked for sharing with us their compiled data on δ 238 U of carbonate and seawater temperature.
Methods and findings We conducted a nationwide, population-based cohort study of all adult patients with septic shock admitted to the ICU in France between January 1, 2014, and December 31, 2018, using the French national hospital database. We matched (within hospitals) in a ratio of 1:up to 4 patients with and without SMI (matched-controls) for age (5 years range), sex, degree of social deprivation, and year of hospitalization. Cox regression models were conducted with adjustment for smoking, alcohol and other substance addiction, overweight or obesity,.
Because of the bias associated with variability and subjectivity of sepsis diagnosis  [20] [21] [22] , there is a need to adjust for severity of illness using an appropriate scoring system  [23]. Finally, patients with SMI are more often hospitalized at university hospitals  [24] [25] [26] , which are characterized by higher sepsis case volumes known to be associated with better survival  [27] , than in smaller hospitals  [24, 25]. Patient matching within a hospital has been advocated to control best for facility confounders  [28]. To the best of our knowledge, to date, no study has determined whether SMI is associated with excess CFR in patients with septic shock after accounting for the most relevant confounding variables. To address this issue, we conducted a nationwide, population-based cohort study using the French national hospital database.