Role of diagnostic labeling in antibiotic prescription.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the association between diagnostic labeling of respiratory tract infections (RTIs) and antibiotic prescription rates in family practice. DESIGN: Descriptive analysis of outpatient chart review supplemented by interviews with physicians. Charts of patients attending 73 general practitioners were reviewed between October 1997 and February 1998. Two days of practice were evaluated per physician. SETTING: Urban family practices in greater St John's, Nfld. PARTICIPANTS: Of 96 family physicians contacted, 73 (76%) agreed to participate. MAIN OUTCOME MEASURES: Rates of diagnoses and antibiotic prescriptions for acute infections. Physicians were divided into "low prescribers" and "high prescribers" based on overall rates of prescription to patients with infections. Low prescribers were compared with high prescribers with respect to physician characteristics, patient characteristics, and diagnoses assigned. RESULTS: Of all patients seen, 22% were seen for acute infections; RTIs accounted for 76% of diagnoses. Low prescribers and high prescribers were of similar ages and saw similar numbers of patients of similar ages with very similar presenting complaints. Both groups diagnosed urinary tract and skin and soft-tissue infections at similar rates, but differed markedly in their rates of diagnoses of RTIs. High prescribers diagnosed bacterial RTIs in 65.4% (147/225) of their patients; low prescribers diagnosed bacterial RTIs in 31.0% (66/213 (P < .001). CONCLUSION: Family doctors frequently prescribe antibiotics. The difference in rates of prescription between high prescribers and low prescribers is largely explained by assignment of diagnoses of RTIs.
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.
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 | 0.000 | 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".