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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".