Bibliographic record
Abstract
<h3>Background</h3> There is a need to reduce antimicrobial uses in humans. Previous studies have found variations in antibiotic (AB) prescribing between practices in primary care. This study assessed variability of AB prescribing between clinicians. <h3>Methods</h3> Clinical Practice Research Datalink, which collects electronic health records in primary care, was used to select anonymised clinicians providing 500+ consultations during 2012–2017. Eight measures of AB prescribing were assessed, such as overall and incidental AB prescribing, repeat AB courses and extent of risk-based prescribing. Poisson regression models with random effect for clinicians were fitted. <h3>Results</h3> 6111 clinicians from 466 general practices were included. Considerable variability between individual clinicians was found for most AB measures. For example, the rate of AB prescribing varied between 77.4 and 350.3 per 1000 consultations; percentage of repeat AB courses within 30 days ranged from 13.1% to 34.3%; predicted patient risk of hospital admission for infection-related complications in those prescribed AB ranged from 0.03% to 0.32% (5th and 95th percentiles). The adjusted relative rate between clinicians in rates of AB prescribing was 5.23. Weak correlation coefficients (<0.5) were found between most AB measures. There was considerable variability in case mix seen by clinicians. The largest potential impact to reduce AB prescribing could be around encouraging risk-based prescribing and addressing repeat issues of ABs. Reduction of repeat AB courses to prescribing habit of median clinician would save 21 813 AB prescriptions per 1000 clinicians per year. <h3>Conclusions</h3> The wide variation seen in all measures of AB prescribing and weak correlation between them suggests that a single AB measure, such as prescribing rate, is not sufficient to underpin the optimisation of AB prescribing.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".