Examining inappropriate medication in UK primary care for type 2 diabetes patients with polypharmacy
Bibliographic record
Abstract
Abstract Aims To estimate the prevalence of potentially inappropriate prescriptions (PIPs) in patients starting their first non-insulin antidiabetic treatment (NIAD) using two explicit process measures of the appropriateness of prescribing in UK primary care, stratified by age and polypharmacy status. Methods A descriptive cohort study between 2016 and 2019 was conducted to assess PIPs in patients aged ≥45 years at the start of their first NIAD, stratified by age and polypharmacy status. The American Geriatrics Society (AGS) Beers criteria 2015 was used for older (≥65 years) and the Prescribing Optimally in Middle-age People’s Treatments (PROMPT) criteria for middle-aged (45-64 years) patients. Prevalence of overall PIPs and individual PIPs criteria was reported using the IQVIA Medical Research Data incorporating THIN, a Cegedim Database of anonymised electronic health records in the UK. Results Among 28,604 patients initiating NIADs, 18,494 (64.7%) received polypharmacy. In older and middle-aged patients with polypharmacy, 39.6% and 22.7%, respectively, received ≥1 PIPs. At the individual PIPs level, long-term PPI use and strong opioid without laxatives were the most frequent PIPs among older and middle-aged patients with polypharmacy (11.1% and 4.1%, respectively). Conclusions This study revealed that patients starting NIAD treatment receiving polypharmacy have the potential for pharmacotherapy optimisation.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 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".