Systemic quinolones and risk of acute liver failure III: A nested case–control study using a US electronic health records database
Bibliographic record
Abstract
BACKGROUND AND AIM: Quinolones are globally popular antibiotics with proven potency, broad coverage, and reasonable safety. However, some concerns were raised as to their possible association with acute liver failure (ALF). The aim of this study is to assess ALF risk within 30 days of receiving a systemically administered quinolone antibiotic, in individuals with no history of liver/diseases. METHODS: We conducted a nested case-control study using electronic health records from the Cerner Health Facts. The initial cohort (n = 35 349 943) included all patients who were admitted between 2000 and 2016, with no history of liver diseases, and had a minimum medical history of one year. Eligible cases were inpatients who were first diagnosed with ALF between 2010 and 2015. Using incidence density sampling, each case was matched with up to five unique controls by sex, race, age at index encounter, and period-at-risk. We used conditional logistic regression to calculate the odds ratio and 95% confidence interval for ALF risk, upon adjusting for exposure to other medications, and major confounders (diabetes mellitus and alcohol abuse). We used the STROBE Statement for reporting on our study. RESULTS: We identified 3151 cases and 15 657 controls. Our primary analysis did not reveal an association between quinolones and ALF risk. However, some risk was identified among those with no or few comorbidities, those ≤ 60 years of age, women, men, African Americans, and Caucasians. CONCLUSION: Although our study does not suggest an overall association between quinolones and ALF, elevated risks seen in some subgroups warrant further investigation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".