How general practitioners would deprescribe in frail oldest-old with polypharmacy — the LESS study
Bibliographic record
Abstract
BACKGROUND: Many oldest-old (> 80-years) with multimorbidity and polypharmacy are at high risk of inappropriate use of medication, but we know little about whether and how GPs would deprescribe, especially in the frail oldest-old. We aimed to determine whether, how, and why Swiss GPs deprescribe for this population. METHODS: GPs took an online survey that presented case-vignettes of a frail oldest-old patient with and without history of cardiovascular disease (CVD) and asked if they would deprescribe any of seven medications. We calculated percentages of GPs willing to deprescribe at least one medication in the case with CVD and compared these with the case without CVD using paired t-tests. We also included open-ended questions to capture reasons for deprescribing and asked which factors could influence their decision to deprescribe by asking for their agreement on a 5-point-Likert-scale. RESULTS: Of the 282 GPs we invited, 157 (56%) responded: 73% were men; mean age was 56. In the case-vignette without CVD, 98% of GPs deprescribed at least one medication (usually cardiovascular preventive medications) stating it had no indication nor benefit. They would lower the dose or prescribe pain medication as needed to reduce side effects. Their response was much the same when the patient had a history of CVD. GPs reported they were influenced by 'risk' and 'benefit' of medications, 'quality of life', and 'life expectancy', and prioritized the patient's wishes and priorities when deprescribing. CONCLUSION: Swiss GPs were willing to deprescribe cardiovascular preventive medication when it lacked indication but tended to retain pain medication. Developing tools for GPs to assist them in balancing the risks and benefits of medication in the context of patient values may improve deprescribing activities in 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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".