Prevalence of potentially inappropriate medications among newly treated patients with type 2 diabetes in UK primary care
Bibliographic record
Abstract
AIMS: The aim of this study was to estimate the prevalence of potentially inappropriate prescriptions (PIPs) in patients starting their first noninsulin 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 Beers criteria 2015 was used for older (≥65 years) patients and the Prescribing Optimally in Middle-age People's Treatments criteria was used 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 anonymized 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 PIP. At the individual PIP level, long-term proton pump inhibitors (PPI) use was the most frequent PIP among older adults, and strong opioid without laxatives was the most frequent PIP in 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 optimization.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".