Antibiotic prescribing for respiratory tract infection across a national primary care network in 2019
Bibliographic record
Abstract
Background: Respiratory tract infection (RTI) is the leading reason for avoidable antimicrobial use in primary care, yet provider-level feedback on its use is only available in some provinces. The aim of this study was to validate case definitions for RTI across the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) and determine baseline provider-level variability in antimicrobial prescribing in 2019. Methods: The RTI case definitions were developed using demographic, diagnostic coding and keywords in electronic medical record. Manual chart abstraction was performed to identify cases of acute otitis media. Remaining RTI definitions were validated using a random sample of 5,164 patients with encounters in 2019. The proportion of patients with an RTI treated with antibiotics was determined by provider, per patient, per episode and per patient encounter. Results: Negative predictive value, positive predictive value and prevalence were as follows: 1.00 (0.99-1.00), 0.99 (0.96-0.99) and 4.14% (4.10-4.19) for common cold; 1.00 (0.99-1.00), 0.94 (0.88-0.98) and 1.09% (1.07-1.12) for acute otitis media; 0.98 (0.96-1.00), 0.93 (0.87-0.97) and 1.2% (1.18-1.22) for acute pharyngitis; 0.99 (0.99-1.00), 0.88 (0.81-0.93) and 1.99% (1.96-2.02) for sinusitis; 0.99 (0.97-0.99), 0.95 (0.89-0.98) and 4.01% (3.97-4.05) for acute bronchitis/asthma. By provider, median (interquartile range [IQR]) proportion treated with antibiotics (per patient) was 6.72 (14.92) for common cold, 64.29 (40.00) for acute otitis media, 20.00 (38.89) for pharyngitis, 54.17 (38.09) for sinusitis, 8.33 (20.00) for acute bronchitis/asthma and 21.10 (20.56) for overall RTI. Conclusion: The CPCSSN can provide national surveillance of antimicrobial prescribing practices for RTI across primary care. Baseline variability underscores the need for provider feedback and quality improvement.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".