Development of a structured clinical pharmacology review for specialist support for management of complex polypharmacy in primary care
Bibliographic record
Abstract
AIMS: Polypharmacy is widespread and associated with medication-related harms, including adverse drug reactions, medication errors and poor treatment adherence. General practitioners and pharmacists cite limited time and training to perform effective medication reviews for patients with complex polypharmacy, yet no specialist referral mechanism exists. To develop a structured framework for specialist review of primary care patients with complex polypharmacy. METHODS: We developed the clinical pharmacology structured review (CPSR) and stopping by indication tool (SBIT). We tested these in an age-sex stratified sample of 100 people with polypharmacy aged 65-84 years from the Clinical Practice Research Datalink, an anonymised primary care database. Simulated medication reviews based on electronic records using the CPSR and SBIT were performed. We recommended medication changes or review to optimise treatment benefits, reduce risk of harm or reduce treatment burden. RESULTS: Recommendations were made for all patients, for almost half (4.8 ± 2.4) of existing medicines (9.8 ± 3.1), most commonly stopping a drug (1.7 ± 1.3/patient) or reviewing with the patient (1.4 ± 1.2/patient). At least 1 new medicine (0.7 ± 0.9) was recommended for 51% patients. Recommendations predominantly aimed to reduce harm (44%). There was no relationship between number of recommendations made and time since last primary care medication review. We identified a core set of clinical information and investigations (polypharmacy workup) that could inform a standard screen prior to specialist review. CONCLUSION: The CPSR, SBIT and polypharmacy workup could form the basis of a specialist review for patients with complex polypharmacy. Further research is needed to test this approach in patients in general practice.
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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 | 0.081 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".