Systemic quinolones and risk of acute liver failure I: Analysis of data from the <scp>US FDA</scp> adverse event reporting system
Bibliographic record
Abstract
BACKGROUND AND AIM: Quinolones are a potent and globally popular group of antibiotics that are used to treat a wide range of infections. Some case reports have raised concern about their possible association with acute hepatic failure (AHF). Data from the US FDA Adverse Event Reporting System were evaluated for signals of AHF in association with systemically administered quinolone antibiotics. METHODS: AHF reports between 1969 and 2019q2, with a focus on 2010-2019q2, were analyzed. Specifically, AHF reports linked to non-quinolone antibiotics of known hepatotoxicity were compared to reports with non-quinolone, non-hepatotoxic (reference) antibiotics; and AHF reports with quinolones were also compared to reports with the same group of reference antibiotics. Two disproportionality signal detection techniques (proportional reporting ratio, PRR, and empirical Bayes geometric mean, EBGM) were used to assess the AHF signal for both analyses. RESULTS: Only ciprofloxacin showed a marginal and significant AHF signal (PRR: 1.85 [1.21, 2.81]; EBGM: 1.54 [1.06, 1.81]); moxifloxacin, levofloxacin, and ofloxacin showed weak and nonsignificant signals. CONCLUSION: Further pharmacovigilance studies are required to confirm the association between ciprofloxacin and AHF seen in the present analysis.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".