How Nova Scotia General Practitioners Choose Antibiotics for the Empirical Treatment of Community‐Acquired Pneumonia
Bibliographic record
Abstract
OBJECTIVE: To gain an understanding of how physicians in general practice choose antibiotics for the empirical treatment of community-acquired pneumonia (CAP). DESIGN: Questionnaire with three sample cases of CAP and a knowledge assessment (mailed to half of the physicians). POPULATION STUDIED: Nova Scotia family physicians. RESULTS: One hundred and eighty-four of the 841 (21.9%) physicians who were mailed a questionnaire responded. A knowledge assessment showed satisfactory knowledge except in two areas - an overestimation of the prevalence of penicillin-resistant Streptococcus pneumoniae in Nova Scotia and the view that ciprofloxacin was an effective antibiotic for the treatment of CAP (42% of physicians). As the complexity of the case increased, there was decreasing consensus regarding the choice of antibiotic therapy and a decline in prescribing according to guidelines for the treatment of CAP. Also, as the complexity of the cases increased, it became increasingly difficult to discern a decision-making strategy. For the simplest case - a 17-year-old male with presumed Mycoplasma pneumoniae pneumonia - physician factors (age, family practice training), desire to target specific pathogens, and concern with resistance and side effects affected the choice of antibiotic. However, for the most complex case - a 45-year-old female with severe pneumonia - familiarity with such a case was the only significant factor and led to treatment with a combination of antibiotics designed to treat both typical and atypical pathogens. CONCLUSIONS: For uncomplicated cases of CAP, physician factors, desire to treat specific pathogens and concern with resistance affect the choice of antibiotic therapy. For complex cases, familiarity with such cases was the only factor that influenced choice of antibiotic therapy.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".