Systematic review and meta-analysis of the proportion of non-typhoidal <i>Salmonella</i> cases that develop chronic sequelae
Bibliographic record
Abstract
The objective of this systematic review and meta-analysis was to estimate the proportion of cases of non-typhoidal salmonellosis (NTS) that develop chronic sequelae, and to investigate factors associated with heterogeneity. Articles published in English prior to July 2011 were identified by searching PubMed, Agricola, CabDirect, and Food Safety and Technology Abstracts. Observational studies reporting the number of NTS cases that developed reactive arthritis (ReA), Reiter's syndrome (RS), haemolytic uraemic syndrome (HUS), irritable bowel syndrome (IBS), inflammatory bowel disease (IBD) or Guillain-Barré syndrome (GBS), Miller-Fisher syndrome (MFS) were included. Meta-analysis was performed using random effects and heterogeneity was assessed using the I 2 value. Meta-regression was used to explore the influence of study-level variables on heterogeneity. A total of 32 studies were identified; 25 reported on ReA, five reported on RS, seven reported on IBS, two reported on IBD, two reported on GBS, one reported on MFS, and two reported on HUS. There was insufficient data in the literature to calculate a pooled estimate for RS, HUS, IBD, GBS, or MFS. The pooled estimate of the proportion of cases of NTS that developed ReA and IBS had substantive heterogeneity, limiting the applicability of a single estimate. Thus, these estimates should be interpreted with caution and reasons for the high heterogeneity should be further explored.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.077 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.048 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".