GPs’ management of polypharmacy and therapeutic dilemma in patients with multimorbidity: a cross-sectional survey of GPs in France
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Bibliographic record
Abstract
BACKGROUND: GPs are confronted with therapeutic dilemmas in treating patients with multimorbidity and/or polypharmacy when unfavourable medication risk-benefit ratios (RBRs) conflict with patients' demands. AIM: To understand GPs' attitudes about prescribing and/or deprescribing medicines for patients with multimorbidity and/or polypharmacy, and factors associated with their decisions. DESIGN AND SETTING: Cross-sectional survey in 2016 among a national panel of 1266 randomly selected GPs in private practice in France. METHOD: GPs' opinions and attitudes were explored using a standardised questionnaire including a case vignette about a female treated for multiple somatic diseases, sleeping disorders, and chronic pain. Participants were randomly assigned one of eight versions of this case vignette, varying by patient age, socioprofessional status, and stroke history. Backward selection was used to identify factors associated with GPs' decisions about drugs they considered inappropriate. RESULTS: Nearly all (91.4%) responders felt comfortable or fairly comfortable deprescribing inappropriate medications, but only 34.7% decided to do so often or very often. In the clinical vignette, most GPs chose to discontinue symptomatic medications (for example, benzodiazepine, paracetamol/tramadol) because of unfavourable RBRs. When patients asked for ketoprofen for persistent sciatica, 94.1% considered this prescription risky, but 25.6% would prescribe it. They were less likely to prescribe it to older patients (adjusted odds ratio [AOR] 0.48, 95% confidence interval [CI] = 0.36 to 0.63), or those with a stroke history (AOR 0.55, 95% CI = 0.42 to 0.72). CONCLUSION: In therapeutic dilemmas, some GPs choose to prioritise patients' requests over iatrogenic risks. GPs need pragmatic implementation tools for handling therapeutic dilemmas, and to improve their skills in medication management and patient engagement in such situations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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