Bibliographic record
Abstract
Background-Repeat prescribing should be limited to drugs which are to be prescribed on a long-term basis to patients whose conditions are stable. Early studies were based on small sample sizes. The definition of repeat prescribing has not been consistent and interpractice variation in repeat prescribing has not been described.Aims-To describe the diagnostic categories and anatomical groups associated with repeat prescriptions; to describe interpractice variation associated with repeat prescribing and to describe the repeat to consultation ratio for the most frequently prescribed diagnoses and drugs.Method-Doctors from a stratified quota sample of 22 Northern Ireland practices recorded their perceived diagnosis for every consultation and for every repeat prescription over a 2-week period.Results-The diagnostic categories significantly associated with repeat prescriptions were digestive, cardiovascular, neurological, psychiatric and metabolic ( p < 0.0001). The anatomical drug categories significantly associated with repeat prescriptions were gastrointestinal drugs, cardiovascular drugs, central nervous system drugs, dressings and appliances (p < 0.0001). There was wide interpractice variation in repeat prescribing (both overall and for individual anatomical groups) and associated diagnoses. High repeat to consultation ratios were recorded for ranitidine, temazepam and diazepan.Conclusions-Wide interpractice variation in repeat prescribing and associated diagnoses revealed poor consensus among practices. Therefore, the approach to the management of common conditions - whether to consult or issue a repeat prescription - was not uniform. The implications of these findings require further research. Commonly occurring diagnoses and drugs had unacceptably high repeat to consultation ratios. Copyright (c) 2000 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".