General practitioners’ deprescribing decisions in older adults with polypharmacy: a case vignette study in 31 countries
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Bibliographic record
Abstract
BACKGROUND: General practitioners (GPs) should regularly review patients' medications and, if necessary, deprescribe, as inappropriate polypharmacy may harm patients' health. However, deprescribing can be challenging for physicians. This study investigates GPs' deprescribing decisions in 31 countries. METHODS: In this case vignette study, GPs were invited to participate in an online survey containing three clinical cases of oldest-old multimorbid patients with potentially inappropriate polypharmacy. Patients differed in terms of dependency in activities of daily living (ADL) and were presented with and without history of cardiovascular disease (CVD). For each case, we asked GPs if they would deprescribe in their usual practice. We calculated proportions of GPs who reported they would deprescribe and performed a multilevel logistic regression to examine the association between history of CVD and level of dependency on GPs' deprescribing decisions. RESULTS: Of 3,175 invited GPs, 54% responded (N = 1,706). The mean age was 50 years and 60% of respondents were female. Despite differences across GP characteristics, such as age (with older GPs being more likely to take deprescribing decisions), and across countries, overall more than 80% of GPs reported they would deprescribe the dosage of at least one medication in oldest-old patients (> 80 years) with polypharmacy irrespective of history of CVD. The odds of deprescribing was higher in patients with a higher level of dependency in ADL (OR =1.5, 95%CI 1.25 to 1.80) and absence of CVD (OR =3.04, 95%CI 2.58 to 3.57). INTERPRETATION: The majority of GPs in this study were willing to deprescribe one or more medications in oldest-old multimorbid patients with polypharmacy. Willingness was higher in patients with increased dependency in ADL and lower in patients with CVD.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it