Assessing the appropriateness of community-based antibiotic prescribing in Alberta, Canada, 2017–2020, using ICD-9-CM codes: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Antimicrobial resistance is a rising threat to human health, and, with up to 90% of antibiotics prescribed in the community, it is critical to examine Canadian antibiotic stewardship practices in outpatient settings. We carried out a large-scale analysis of appropriateness in community-based prescribing of antibiotics to adults in Alberta, reporting on 3 years of data from physicians practising in the province. METHODS: (ICD-9-CM), as used for billing purposes by the province's fee-for-service community physicians, to drug dispensing records, as maintained in the province's pharmaceutical dispensing database. We included physicians practising in community medicine, general practice, generalist mental health, geriatric medicine and occupational medicine. Following an approach used in previous research, we linked diagnosis codes with antibiotic drug dispensations, classified across a spectrum of appropriateness (always, sometimes never, no diagnosis code). RESULTS: We identified 3 114 400 antibiotic prescriptions dispensed to 1 351 193 adult patients by 5577 physicians. Of these prescriptions, 253 038 (8.1%) were "always appropriate," 1 168 131 (37.5%) were "potentially appropriate," 1 219 709 (39.2%) were "never appropriate," and 473 522 (15.2%) were not associated with an ICD-9-CM billing code. Among all dispensed antibiotic prescriptions, amoxicillin, azithromycin and clarithromycin were the most commonly prescribed drugs labelled "never appropriate." INTERPRETATION: We found that nearly 40% of prescriptions dispensed to 1.35 million adult patients in Alberta's community-based settings over a 35-month period were inappropriate. This finding suggests that additional policies and programs to improve stewardship among physicians prescribing antibiotics for adult outpatients in Alberta may be warranted.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".