dataesr/scientific-paragraphs-categorization huggingface dataset:
A limitation is that statistical power was low in the analyses of ischemic stroke subtypes. The power was particularly low in the analyses of calcium because the SNPs only explained a small proportion of the variance (0. 9%) in serum calcium levels. Hence, we cannot rule out that we may have overlooked weak associations between genetically predicted serum calcium concentrations and ischemic stroke subtypes. Another shortcoming is that the biological function of several of the genetic loci associated with serum magnesium and calcium levels are unknown (data available from Open Science Framework, table e-9, osf. io/b57sq/). The reliability of MR results relies on 3 main assumptions (data available from Open Science Framework, figure e-1, osf. io/b57sq/), which can be violated by population stratification, canalization, and pleiotropy.
RAPD-PCR DNA from the tested L. plantarum strains was isolated with the kit "DNA sorb" (Amplisens, Russia) and amplified with the primer M13  (Ben Omar et al. , 2008)  in 35 cycles of 94°C for 1 min, 45°C for 1 min and 72°C for 2 min (with preheating at 94°C for 3 min and post-elongation time for 5 min at 72°C). A thermocycler "MyCycler" was used (BioRad, USA). The products were separated by agarose electrophoresis (1% of agarose in Tris-acetate buffer), and the sizes of amplicons were measured using the GelAnalyzer2010 program. The phylogenetic tree was constructed using the program Mega5  (Tamura et al. , 2007 (Tamura et al. , , 2011) )  basing on the method of maximum likelihood (ML)  (Tamura et al. , 2011)  and the Jukes-Cantor model  (Jukes and Cantor, 1969). The statistical significance of the order of branching of the tree has been estimated using bootstrap analysis by constructing of the 1000 alternative replicas, or trees.
S33 Public Health Action (Cepheid, Sunnyvale, CA, USA). Only specimens in which rifampicin resistance is detected by Xpert are sent to the Queensland Mycobacterium Reference Laboratory (Brisbane, QLD, Australia) for mycobacterial culture and drug susceptibility testing. Laboratory investigation of extrapulmonary TB is dependent on the disease site, such as fine needle aspiration of the lymph nodes or examination of cerebrospinal fluid. Routine HIV testing is encouraged, but not always performed due to shortages of trained staff (certified training is required), the volume of patients and inconsistencies in data recording.
Data collection and analysis The data variables collected in this study included the patients' residence, age, sex, weight, weight for age, TB site, type of TB, HIV status and treatment outcomes, which were reported according to the standard WHO and national definitions. 9, 11 The data were captured in e-TB Manager (SIAPS, Arlington, VA, USA) tablets that were introduced to PMGH together with the new FDC in August 2016 by the Remote Sensing Centre (Port Morseby, PGN) and the National Health Information System (Port Morseby, PGN). After capture, the data were downloaded into an electronic database and then transferred into Microsoft Excel (Microsoft Corp, Redmond, WA, USA). The data were cross-checked with the treatment registers, the follow-up clinic registers and paediatric admission and inpatient death register books.
RESULTS Of 713 children who initiated treatment with the new FDCs over a 1-year period, 554 (78%) were recorded as    1 ); 117 (16%) of the study population were infants aged 12 months. Pulmonary TB (68% of total cases) was the most common site and extrapulmonary TB included lymph node TB (9% of total cases), TB meningitis (7%), abdominal TB (4%), and pleural TB (2%). Less than 1% of the cohort had bacteriologically confirmed TB. HIV status was unknown in 50% of the cohort, and among 357 children with known HIV status, 13% were living with HIV. Table  3  shows the data on treatment outcomes. There were no children recorded as 'cured' as sputum was not collected for microscopy or culture at follow-up. There were 25 deaths. The median time from starting treatment until death was 10 days, although there a wide range [IQR 6-53].
Of the children who died, 15 (60%) had pulmonary TB and 7 (28%) had disseminated disease, 5 with TB meningitis and 2 with miliary TB. Cause of death for the remaining 3 children was unknown. Seven (28%) of the deaths were in children newly diagnosed with HIV, 9 (36%) were HIV-negative and for 9 HIV status was unknown. One third (34%) of all children in the cohort were lost to follow-up. The characteristics associated with the outcomes of 'treatment complete' (n = 425) and 'LTFU' (n = 242) were assessed (Table 4). Children who were severely underweight (weight-for-age Z score -3) on presentation were at significantly greater risk of LTFU compared to children of normal weight on multivariable analysis adjusting for potential confounders (adjusted RR [aRR] 1. 3, 95%CI 1. 0-1. 6, P  0. 05). Multivariable analysis similarly adjusted for potential confounders did not identify any factors associated with unfavourable outcomes, defined collectively as died, LTFU, and not evaluated (data not shown).
Another advantage of IPD is that they usually require an international collaborative effort, involving trial authors, who may help to identify more relevant trials, and also contribute to an agreed analysis plan and shared understanding of the results. While failure to obtain some datasets may lead to selection bias if there are systematic reasons why some studies do not provide full data, methods have been developed to combine IPD with aggregate data (when IPD is not available for some studies) in NMA  (Donegan, Williamson, D'Alessandro, & Smith, 2012; Sutton, Kendrick, & Coupland, 2008). We decided in collaboration with several authors of primary trials that there would be value in conducting an IPD metaanalysis to explore the question of whether mass deworming is more effective for subgroups of children defined by characteristics such as infection intensity or status, age or nutritional status.
| Types of study designs We included randomised and quasirandomised trials. For the purpose of determining whether specific individual-level and environmentlevel characteristics are associated with greater effects of deworming, there is sufficient evidence from over 70 randomised trials with over 100,000 children to include only randomised and quasirandomised trials. We included studies reported in abstract form at a conference as well as unpublished studies. We sought full datasets from all studies and carried out the same methods for data checking and quality for all studies.
| Duration of follow-up For weight and height, we included data from studies >4 months in duration because we considered this as a minimum duration to observe differences in growth based on clinical expertise of nutritionists on the team and decided a priori. However, for haemoglobin and ferritin status, changes may occur sooner, so study duration was not be used as an exclusion criterion. While infection status and infection intensity are affected much sooner than this, these are not primary outcomes of interest since there is no question that deworming drugs reduce infection load. We assessed infection intensity and status at baseline as indicators of the force of infection in the population. We collected data at each available time-point and aimed to explore study duration as a covariate in the model.
| Statistical procedures and conventions Data were prepared into a Microsoft Excel spreadsheet with the same fields for every study. Since we only included RCTs, we considered the missing values for each variable as missing at random (MAR) based on observed data  (Joshi, Royuela, & Zamora, 2013). We used multiple imputation to impute the missing values for baseline and outcome variables based on the assumption of MAR  (Bell, Fiero, Horton, & Hsu, 2014; Groenwold, Moons, & Vandenbroucke, 2014; Jakobsen, Gluud, Wetterslev, & Winkel, 2017)   We accounted for clusters (such as villages, schools or households) as nested within each study. We analysed IPD datasets to check for comparability with the primary published papers. We calculated the standardised difference between the published data and the IPD received from authors for baseline characteristics and baseline outcome assessment.
For endline, we replicated the effect measures reported in study publications and calculated the standardised difference between the IPD received and the study report  (Austin, 2009). As with our previous Campbell review, we used a two-step process to meta-analysis. We conducted pairwise analyses for each comparison of interest by entering all IPD data into a multilevel model, with each study as one cluster. We expected considerable heterogeneity between studies for each outcome based on our Campbell review; therefore, we used a random effects model. We assessed mean differences in change from baseline for weight (kg), height (cm) and haemoglobin (g/L). We intended to assess plasma ferritin (mcg/L) but too few studies reported this outcome (seven studies with 6,318 participants). The Advisory board, based on clinical and methodological expertise, decided that there were insufficient studies to conduct effect modification analyses and that basic random effects meta-analysis could be misleading.
| Subgroup analyses Provided sufficient data was available to inform the evidence network, subgroup analyses were conducted to assess effects across both child-level as well as environment-level characteristics. We compared the results of models with subgroup analyses by assessing the size of quantitative or qualitative differences in effects, the statistical significance of tests for interactions, assessing betweenstudy variance and assessing the goodness of fit of the models using the likelihood ratio. The following child and environment level effect modifiers were planned: Child level: • Individual-level intensity of infection with ascaris, trichuris and hookworm (across four levels of none, light, moderate and heavy, using the WHO cutoffs for each helminth (http://apps. who. int/iris/ bitstream/10665/44671/1/9789241548267_eng.
We decided this was not possible therefore we did not do a planned sensitivity analysis with children in the poorest tertile. Before conducting subgroup analyses, we assessed the distribution of each variable. If there were insufficient children in some categories, the levels were combined (see results). We planned to assess socioeconomic status of household or parents and maternal education as effect modifiers, but data was insufficient (see results). Environment level: • Study level sanitation and hygiene environment, as reported by studies was assessed to consider whether environments can be classified according to consistent system • Study-level prevalence (using WHO cut-offs for each worm-type, as above) • Study-level intensity of infection (using WHO cut-offs for each worm-type, as above) As noted in Table  1 , environment level characteristics were not entered into the model.
| Sensitivity analyses Provided sufficient data was available to inform the evidence network, we conducted sensitivity analyses to assess robustness of results when restricted to studies at low risk of bias for sequence generation, allocation concealment and blinding of participants. We assessed whether results were robust to excluding imputed data (i. e. , complete case analysis). We assessed sensitivity to restricting to studies published in 2008 or later (last 10 years). Data were housed at a secure data warehouse at the Bruyère Research Institute, following the personal health information act. Data were transferred to SAS as a common platform for all studies, using a common data dictionary. V. W. checked IPD data for consistency immediately upon receiving datasets. For example, we checked for outlier individuals (e.
| Search results We searched all databases up to March 27, 2018. We also retrieved in full text all 299 primary studies included in eight previous reviews (Danso-Appiah, Olliaro,  Donegan, Sinclair, & Utzinger, 2013; Grimes et al. , 2014; Kramer, Zhang, Sinclair, & Olliaro, 2014; Salam, Haider, Humayun, & Bhutta, 2015; Salam, Maredia, Das, Lassi, & Bhutta, 2014; Strunz et al. , 2014; Taylor-Robinson et al. , 2015; Welch et al. , 2016). We screened 14,034 records for inclusion. We screened 340 studies in full-text. We assessed 41 studies of deworming for STH and 14 studies of schistosomiasis treatment as eligible for inclusion. One study included treatments for both STH and schistosomiasis  (Olds et al. , 1999) , and is included in both counts (Figure  2 ). A total of 285 studies were excluded because they did not meet eligibility criteria, due to lack of infection intensity data (n = 14), <3 months (n = 9) and wrong study design (n = 262; Table  S3 ).
| Contacting authors and yield of studies We contacted first authors of all eligible studies by email outlining the study purpose and inviting them to join the Deworming Collaborative. If there was no reply, contact with all authors was then made by email or using other contact information such as Researchgate and twitter. Authors were contacted in their language if possible (French, Portuguese). For STH, we received complete data from 19 out of 41 published studies (46%) (Tables  S5 ), which represented 79% of all children included in those eligible STH trials (Figure  3 ). The retrieval of data was better for studies conducted after 2000, with a yield of 15 out of 22 published studies (68%) and 90% of participants randomised to eligible studies (Figure  4 ). For studies conducted before 2000, we received only four out of 19 studies (21%), and 39% of participants randomised (Figure  5 ).
| Aggregate effect estimates of studies not providing IPD We compared the effect estimates of the studies which were eligible but did not provide data, those that provided data and those which were not eligible (no infection intensity, too small or too short). Results for STH deworming versus placebo for weight gain (kg) are shown in Figure  7. As can be seen on visual inspection, two studies had much larger effects on weight gain than any others  (Stephenson, Latham, Adams, Kinoti, & Pertet, 1993; Stephenson, Latham, Kurz, Kinoti, & Brigham, 1989). The heterogeneity with both of these studies included was 90% as assessed by I 2 , suggesting that statistical combining of these studies is inappropriate. As in our previous Campbell review, we removed outliers to assess contribution to I 2. Removing  Stephenson (1989) , which we earlier assessed as having imbalance in baseline covariates which may have influenced results  (Welch et al.
| Feasibility of conducting IPD meta-analysis We judged that we had insufficient data to conduct analysis of the studies of deworming for schistosomiasis since we received only two studies out of 13 eligible for analysis, and this represented 36% of participants randomised to eligible studies. For deworming for STH, we received IPD from 19 studies out of 41 considered eligible (46%) and 31,945 out of 40,525 participants randomised (79%). We considered this was sufficient data to pursue IPD meta-analysis.
| Quality of studies Overall, there was low risk of selection and performance bias in 47% (9 of 19) studies. 47% (9 studies) had unclear risk of bias due to lack of detail on allocation method or method of blinding. Overall, there was a high risk of attrition bias in 37% (seven studies) of the included studies. Attrition bias was judged high risk due to loss to follow-up of >20% of participants in these studies. Detection bias could not be assessed in 58% (11 studies) of the studies and selective reporting could not be assessed in 79% (15 studies) due to insufficient information. No major baseline imbalance was found in 74% (  14 studies) of the studies, judged according to the description of baseline characteristics (Figures  8  and 9 ). The overall risk of bias was similar for studies for which we were unable to obtain data except for selection bias which was low risk in.
| Effect of deworming on infection intensity The effect of deworming on infection intensity was assessed for each study and each type of STH infection and found to be variable across studies (see Appendix 4, Forest plots, comparisons 9 and 10). When sorted according to year of publication, there was no visual trend of greater infection prevalence between the years of publication of these studies from 1999 to 2015. The complete case analysis (only including children with complete data on infection prevalence) were comparable to results conducted using multiple imputation for baseline prevalence. Six studies had relative risk reduction of A. lumbricoides infection prevalence 20% or more when compared to the placebo group in A. lumbricoides prevalence at endline  (Beasley et al. , 1999; Friis et al.
For example, the effect on weight for STH deworming versus placebo was 0. 01 kg (95% CI: -0. 11, 0. 12). Analysis of the NMA model restricted to studies at low risk of bias yielded similar results (Table  S12 ) for weight gain (kg) for STH versus placebo (0. 01 kg, 95% CI: -0. 10, 0. 12). However, there were F I G U R E 1 3 Final collapsed evidence network. *Five studies with >50% missing data are not shown in this figure. Four of these included STH deworming versus placebo  (Wiria, Kirwan, Miguel, Rousham)  and one assessed STH deworming + micronutrients versus micronutrients  (Hall et al. , 2006). STH, soil-transmitted helminthiasis  We assessed the full evidence network with 18 nodes with our 14 base case studies, with multiple imputation for missing data and adjusted for covariates as a sensitivity analysis to allow comparison of separate drugs such as albendazole and mebendazole at different frequencies to our main model findings with the collapsed network.
As described above, there was variation in effect of deworming on infection prevalence at endline. We conducted a senstivity analysis restricted to studies which were more effective at reducing infection prevalence, defined as a a relative risk of 0. 80 or lower when compared to the placebo group in A. lumbricoides prevalence at endline  (Beasley et al. , 1999; Friis et al. , 2003; Le Huong et al. , 2007; Nga et al. , 2009; Stoltzfus et al. , 1997 Stoltzfus et al. , , 2004)). The results of this sensitivity analysis show that for STH deworming versus placebo, the effect on weight gain was 0. 08 kg, 95% CI (-0. 10, 0. 26), whereas our basecase analysis findings were 0. 10 kg (95% CI: -0. 08, 0. 11). Comparison of effect sizes for weight gain (kg) between received data and studies that were not included in the analysis We assessed whether the effects on weight gain were similar for these studies which did not provide data (either because they did not provide it or because they did not meet eligibility criteria) to the studies which did provide data (Appendix 3).
Sensitivity analyses There were no qualitative (different directions of effects) nor quantitative (different sizes of effects) differences in any of the sensitivity analyses including: (a) unadjusted analyses, (b) complete case (unadjusted), (c) studies at low risk of bias, (d) full model with 18 nodes and (e) complete case with additional five studies that had too much missing data to be included in the adjusted models (Tables  S12  and S13 ). As an example, the effect sizes for the STH versus placebo comparison for height gain are in the table below (Table  14 ). Comparison of effect sizes for height gain (cm) between received data and studies that did not provide data We assessed whether the effects on height gain were similar for these studies which did not provide data (either because they did not provide it or because they did not meet eligibility criteria) to the studies which did provide data (Appendix 3).
Sensitivity analyses All sensitivity analyses were congruent with these main findings including the complete case model (14 studies, six nodes, unadjusted), complete case with additional five studies (unadjusted) and the unadjusted 14 study model (Table  16 ). Analysis of the six studies with greatest impact on A. lumbricoides prevalence at endline (relative risk of 0. 80 or greater when compared Comparison of effect sizes for haemoglobin (g/L) between received data and studies that did not provide data F I G U R E We assessed whether the effects on haemoglobin were similar for these studies which did not provide data (either because they did not provide it or because they did not meet eligibility criteria) to the studies which did provide data (Appendix 3). The test for interaction for subgroup differences for STH deworming versus placebo was not statistically significant (p =.
| Year of publication We planned to restrict our IPD-NMA to studies conducted 2008 or later. However, we decided that it would be more informative to conduct a meta-regression using aggregate data according to year of publication to include older studies for which we were unable to obtain individual participant datasets. This analysis shows a negative association, with a greater effect in older studies which was not statistically significant (p =. 05) and explained 7. 88% of the variance between studies. The graph shows a concentration of more recent studies with smaller effects on weight gain.
In a sensitivity analysis, we included these studies to assess the influence on our results, and our random effects meta-analysis was 0. 10 kg (95% CI: 0. 03, 0. 17). Note: these studies are included in the NMA-IPD presented in this paper if they had baseline infection intensity and provided data (that is,  Hall et al. (2006)   We were successful in retrieving 14% (2/14 studies) for deworming for schistosomiasis. As a result, we were unable to conduct our planned analysis of deworming for schistosomiasis.
| Overall completeness and applicability of evidence For STH deworming, we received data for 19 studies with 31,945 participants of an eligible 41 studies with 40,132 participants for studies of STH deworming. Fifteen of the 19 studies were published in the last 15 years. We had sufficient participants in each level of our planned effect modifier analyses to run all of our planned effect modification analyses using the base case evidence network. Although there were no statistically significant differences across infection intensity levels, these were limited due to the paucity of children with moderate to heavy infection intensity in our sample (<13%). Furthermore, the upper CIs included potentially important effects of up to 460 g for weight and 7 g/L for haemoglobin. In direct evidence comparisons using WHO cutoffs, we also did not find statistically significant interaction across intensity of infection, however, there were larger effects on weight gain for children with moderate or heavy intensity infections of A.
lumbricoides or T. trichuria. For prevalence of A. lumbricoides and hookworm, we also assessed whether there was a relationship between prevalence and effects on weight using metaregression for aggregate data for all studies with weight data for STH deworming versus placebo. These analyses did not show a relationship with A. lumbricoides prevalence at the study level. There was an association of higher hookworm prevalence with effect on weight. These meta-regressions must be interpreted with caution since they are using data at the aggregate level  (Debray et al. , 2018). We conducted an extensive search of electronic databases, with advice from the Campbell Collaboration International Development Group information scientist. We screened 16,613 articles and updated this search to March 27, 2018.