Shiga Toxin–Producing<i>Escherichia coli</i>Infection, Antibiotics, and Risk of Developing Hemolytic Uremic Syndrome: A Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Antibiotic administration to individuals with Shiga toxin-producing Escherichia coli (STEC) infection remains controversial. We assessed if antibiotic administration to individuals with STEC infection is associated with development of hemolytic uremic syndrome (HUS). METHODS: The analysis included studies published up to 29 April 2015, that provided data from patients (1) with STEC infection, (2) who received antibiotics, (3) who developed HUS, and (4) for whom data reported timing of antibiotic administration in relation to HUS. Risk of bias was assessed; strength of evidence was adjudicated. HUS was the primary outcome. Secondary outcomes restricted the analysis to low-risk-of-bias studies employing commonly used HUS criteria. Pooled estimates of the odds ratio (OR) were obtained using random-effects models. RESULTS: Seventeen reports and 1896 patients met eligibility; 8 (47%) studies were retrospective, 5 (29%) were prospective cohort, 3 (18%) were case-control, and 1 was a trial. The pooled OR, including all studies, associating antibiotic administration and development of HUS was 1.33 (95% confidence interval [CI], .89-1.99; I(2) = 42%). The repeat analysis including only studies with a low risk of bias and those employing an appropriate definition of HUS yielded an OR of 2.24 (95% CI, 1.45-3.46; I(2) = 0%). CONCLUSIONS: Overall, use of antibiotics was not associated with an increased risk of developing HUS; however, after excluding studies at high risk of bias and those that did not employ an acceptable definition of HUS, there was a significant association. Consequently, the use of antibiotics in individuals with STEC infections is not recommended.
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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 | 0.020 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.063 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".