Defining appropriate antibiotic prescribing in primary care: A modified Delphi panel approach
Bibliographic record
Abstract
Background: Antimicrobial overuse contributes to antimicrobial resistance. In the ambulatory setting, where more than 90% of antibiotics are dispensed, there are no Canadian benchmarks for appropriate use. This study aims to define the expected appropriate outpatient antibiotic prescribing rates for three age groups (<2, 2-18, >18 years) using a modified Delphi method. Methods: We developed an online questionnaire to solicit from a multidisciplinary panel (community-academic family physicians, adult-paediatric infectious disease physicians, and antimicrobial stewardship pharmacists) what percentage of 23 common clinical conditions would appropriately be treated with systemic antibiotics followed with in-person meetings to achieve 100% consensus. Results: The panelists reached consensus for one condition online and 22 conditions face-to-face, which took an average of 2.6 rounds of discussion per condition (range, min-max 1-5). The consensus for appropriate systemic antibiotic prescribing rates were, for pneumonia, pyelonephritis, non-purulent skin and soft tissue infections (SSTI), other bacterial infections, and reproductive tract infections, 100%; urinary tract infections, 95%-100%; prostatitis, 95%; epididymo-orchitis, 85%-88%; chronic obstructive pulmonary disease, 50%; purulent SSTI, 35%-50%; otitis media, 30%-40%; pharyngitis, 18%-40%; acute sinusitis, 18%-20%; chronic sinusitis, 14%; bronchitis, 5%-8%; gastroenteritis, 4%-5%; dental infections, 4%; eye infections, 1%; otitis externa, 0%-1%; and asthma, common cold, influenza, and other non-bacterial infections (0%). (Note that some differed by age group.). Conclusions: This study resulted in expert consensus for defined levels of appropriate antibiotic prescribing across a broad set of outpatient conditions. These results can be applied to community antimicrobial stewardship initiatives to investigate the level of inappropriate use and set targets to optimize antibiotic use.
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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 | 0.189 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".