The effect of obesity on antibiotic treatment failure: a historical cohort study
Bibliographic record
Abstract
PURPOSE: Obesity, a major health issue, is also an important risk factor for infections. Evidence demonstrates that excess weight affects the disposition of antibiotics but little work has been done to explore if this results in antibiotic treatment failure (ATF). ATF has serious adverse health outcomes and may increase treatment resistance. Given that obese patients often have other health issues, it is important to determine if excess weight independently increases the likelihood of ATF. METHODS: Consenting patients (N = 18 014), randomly sampled from Santé Québec Health surveys (1992, 1998), were linked with administrative health databases. Patients were within the normal, overweight, and obese weight categories aged 20-79 years old, receiving at least one course of antibiotic therapy from the survey date until December 2005. ATF was defined as any additional antibiotic prescriptions or hospitalizations for infections within the 30 days after initial therapy. Logistic regression was used to assess the impact of excess weight on ATF after adjusting for patient characteristics, comorbidities, history of antibiotic use, antibiotic resistance, and flu season. RESULTS: Of the final sample size (N = 6 179), 39.0% were overweight and 21.4% were obese. The most frequently prescribed antibiotics were amoxicillin (16.0%), ciprofloxacin (9.2%), phenoxymethylpenicillin (8.8%), trimethroprim/sulfamethoxazole (8.6%), and clarithromycin (8.5%). ATF occurred in 828 (13.4%) of the 6 179 study patients. Obesity was a significant predictor of ATF (adjusted OR 1.26; 95% CI 1.03-1.52). CONCLUSION: Obesity is a significant risk factor for ATF, and this association may be due to the current "one size fits all" dosing strategy, which warrants 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".