Antibiotic utilisation in community practices: guideline concurrence and prescription necessity
Bibliographic record
Abstract
PURPOSE: To evaluate the indications, concurrence with prescribing guidelines and potential necessity for antibiotic (AB) prescriptions written in community practice. METHODS: We reviewed the charts of all patients with infection-related illnesses seen by family physicians during two random days of regular practice between 1 Oct 1997 and 30 Jan 1998. Guideline concurrence of AB prescribing was assessed using regional AB prescribing guidelines. Likelihood of AB indication for respiratory tract infections was assessed using published clinical practice guidelines for determination of likely viral versus bacterial etiology. RESULTS: Of 4218 visits captured, 949 (22%) were for newly acquired infections. Sixty four percent (n=604) of consultations for newly acquired infections resulted in an AB prescription. Based on the doctors' diagnoses, 61% of AB prescriptions were concurrent with prescribing guidelines, 10% were for the wrong drug, 20% were not indicated and in 10% of cases a lower line AB was available. For respiratory tract infections, 12% of these infections were likely bacterial, whereas the physicians determined that 56% were bacterial. CONCLUSIONS: A large proportion of ABs administered in community practices were not in concurrence with community AB prescribing guidelines. Improvements can be made in AB choice and in decisions about likely viral etiology for respiratory tract infections.
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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 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".