Are general practitioners willing to deprescribe in oldest-old patients with polypharmacy?
Bibliographic record
Abstract
Abstract Managing the growing number of oldest-old patients with multimorbidity and polypharmacy in primary health care poses an increasing public health challenge. Since inappropriate polypharmacy can harm patients’ health, general practitioners (GPs) should regularly review patients’ medications and, if necessary, deprescribe. This case vignette study evaluates the deprescribing decisions of GPs from 31 countries and compares the factors influencing GPs’ deprescribing decisions. We invited GPs to participate in an online survey, containing a) three cases of increasingly dependent oldest-old multimorbid patients with potentially inappropriate polypharmacy and b) Likert-scale questions assessing the importance of factors influencing deprescribing. We presented each case with and without history of cardiovascular disease (CVD). For each case, we asked whether GPs would deprescribe any medication and, if so, which one(s). We calculated percentages of GPs deprescribing at least one medication in each case, compared cases with/without CVD history and different levels of dependency in activities of daily living, and calculated the percentage of factors rated as important or very important. Of 3175 invited GPs from 31 countries, 53% responded (N = 1’706) with a mean age of 50 years and 60% females. Results are preliminary, but despite some differences across GP characteristics (male/female, age) and across countries, GPs generally showed a high willingness to deprescribe in oldest-old patients (>80 years) with polypharmacy. GPs were more likely to deprescribe in patients with a higher level of dependency, in the absence of history of CVD, and when patients are on statins, proton-pump inhibitors or potentially inappropriate pain medication. Factors GPs rated as important or very important for the deprescribing decision were patients’ quality of life, risks and benefits of medications, patients’ life expectancy, and potential negative health outcomes resulting from deprescribing. Key messages Despite international differences, most GPs report they would deprescribe in older multimorbid patients with polypharmacy. The results will facilitate the development of interventions supporting general practitioners to deprescribe.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".