The effect of obesity on antibiotic treatment failure: a historical cohort study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it