Why antibiotics should not be used to treat Shiga toxin-producing Escherichia coli infections
Bibliographic record
Abstract
PURPOSE OF REVIEW: There has been much debate about treating Shiga toxin-producing Escherichia coli (STEC) infections with antibiotics. No data convincingly demonstrate that antibiotics are better than no antibiotic treatment at all, and many studies suggest antibiotics increase the risk of developing the hemolytic uremic syndrome (HUS). This topic is timely, because emerging technology enables rapid identification of STEC-infected patients, and we anticipate questions about management will increase. This review is designed to familiarize readers with the series of observations that underlie our recommendations. RECENT FINDINGS: The long debate over antibiotics in STEC infections appears resolved by gradually accruing information that show that antibiotics do not benefit infected patients. In fact, they are associated with an increased likelihood of developing HUS. A meta-analysis published in 2016 demonstrated that low risk of bias studies find a clear association between antibiotic use and development of HUS. Subsequent publications do not refute these findings. SUMMARY: In high-income countries, antibiotics should not routinely be given to patients with acute diarrhea unless testing demonstrates a pathogen for which antibiotics are indicated, and STEC infection has been excluded. Future work to prevent HUS should focus on preventing primary infections, and mitigating extraintestinal consequences of STEC gut infections.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".