Antibiotic prescribing for pediatric respiratory infections: What explains a large variation among physicians?
Bibliographic record
Abstract
OBJECTIVE: To explore whether there are observable physician characteristics associated with antibiotic prescribing for pediatric respiratory tract infections (RTIs). DESIGN: Population-based cohort study using a hierarchical generalized linear mixed-model analysis. SETTING: British Columbia. PARTICIPANTS: All pediatric visits for RTIs between 2005 and 2011. MAIN OUTCOME MEASURES: The association between an antibiotic prescription being dispensed within 5 days after each visit and patient, physician, and regional characteristics. RESULTS: Overall, 27.9% of RTI visits were followed by an antibiotic prescription. After accounting for observed patient, physician, and regional factors, median 2-fold variation was found across physicians in their odds of prescribing. Observable physician characteristics explained nearly half of the variation between them. Higher prescribing was evident among physicians with more years of clinical experience (odds ratio [OR] of 1.46, 95% CI 1.33 to 1.61), international medical graduates (OR = 1.73, 95% CI 1.63 to 1.83), and physicians with proportionally fewer recent visits for RTIs (OR = 1.45, 95% CI 1.38 to 1.52). Female physicians prescribed less often than male physicians did (OR 0.91, 95% CI 0.86 to 0.96). CONCLUSION: Substantial variations were found among physicians in prescribing antibiotics for pediatric RTIs. Observable characteristics accounted for a meaningful proportion of this variation; however, some physicians have a higher propensity to prescribe than others do, which remains unexplained. Patient and regional characteristics did not explain much of the variation across physicians. In future, behavioural interventions should be designed and evaluated to target physicians with higher propensity to prescribe.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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".