Bibliographic record
Abstract
Objectives: To determine whether excess weight is associated with antibiotic treatment failure (ATF) and if this may be due to a lack of weight-based dosing.Methods: Using a historical cohort study design, data linked to Quebec administrative databases were available for 18 014 consenting patients randomly sampled from the 1992 and 1998 Santé Québec Health surveys (response rate 85%). Selected patients were within the normal weight, overweight and obese weight classes aged 20–79 years, receiving at least one episode of antibiotic therapy from the health survey date to December 2005. ATF was measured via secondary antibiotic prescriptions or additional hospitalizations for infections within the month following initial therapy for each participant. The antibiotic daily dose (DD) and daily dose to body mass index (DD:BMI) ratios were computed for those receiving an oral antibiotic prescription. Logistic regression was performed to determine whether overweight and/or obesity as well as dosing factors (e.g. DD:BMI) were significant predictors of ATF, while one-way ANOVA with Tukey-Kramer adjustment for multiple comparisons was used to determine if DD:BMI ratios differed significantly across weight groups, reflecting a lack of weight-based dosing. Results: Of the 6 179 patients selected, 828 (13.4%) had an ATF event during the outcome assessment period. Obesity was found to be a significant predictor of ATF with an OR of 1.26 (95% CI 1.03-1.52), after adjusting for other potential confounders including sociodemographic, and antibiotic-related factors (e.g. MRSA and history of antibiotic use). The antibiotic DD:BMI ratio means differed significantly between weight groups, where means decreased with increasing BMI. When included in the ATF predictive model along with other previous confounding factors, the DD:BMI variable was significant (p-value of 0.03) with a modest adjusted OR of 1.004 (95% CI 1.000-1.007). Conclusions: Obesity is a significant predictor of ATF and this association is likely due to the current “one size fits all” dosing strategy. Findings may encourage further research in the field of pharmacokinetics and family medicine to find a means of standardizing current antibiotic dosing guidelines for weight as well as implementing weight-based dosing in family practice.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".