90 Reduction of inappropriate antibiotic prescribing in a GP practice led by practice pharmacists
Bibliographic record
Abstract
<h3>Aim</h3> Establish: The proportion of total volume of antibiotics* The proportion of broad spectrum* antibiotics prescribed Ensure local targets were met (Please see results) despite a growing list size To promote antibiotic stewardship <h3>Methods</h3> CCG data on antibiotic prescribing was reviewed at regular intervals. Baseline data was taken from the period of July 2018 to December 2018. The CCG used EPACT data and adjusted per 1000 STARPU. The following actions were then taken as a result of this data: Antibiotic prescriptions were monitored on a weekly basis against NICE antibiotics guidelines. Prescriptions outside of this guidance were reviewed further for appropriateness. Learnings were shared with individual prescribers & the wider team (there were approximately 100 prescribers at the practice in November 2018) Discouraging delayed antibiotic prescribing (improved access means patients are able to book subsequent appointments easily if necessary) These actions were driven by two practice pharmacists. <h3>Results</h3> Quantity of co-amoxiclav, cephalosporin and quinolone items*: The quantity reduced by 9 points*(36%) (p<0.001)(Target < 40) Quantity of total antibacterials*: The quantity reduced by 131* (30%) (p<0.001)(Target < 350) *Quantity per 1000 antibacterial STAR PU (From Hammersmith and Fulham CCG data) <h3>Discussion/conclusion</h3> All results were per 1000 registered users and were STAR PU adjusted (specific therapeutic age-sex related prescribing unit) allowing us to compare with other practices in the locality. Monitoring of antibiotics and sharing learnings on an ongoing basis by practice pharmacists has made a statistically significant impact on reducing the number of antibiotics prescribed and so assisted in antibiotic stewardship. Based on this we are sharing the learnings with our practices in Rwanda and Canada with an aim to safeguard antibiotic stewardship globally.
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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 | 0.001 |
| 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".