Perspectives on the Influence of Pharmaceutical and MedTech Companies on Deprescribing Decisions and Conference Sponsorship: A Survey Study
Bibliographic record
Abstract
BACKGROUND: The involvement and sponsorship of pharmaceutical and medical technology (MedTech) companies in deprescribing and medication optimization activities raise questions about conflicts of interest. We surveyed registrants and attendees of previous International Conferences on Deprescribing to explore views on the acceptability and impact of such involvement and sponsorship. METHODS: We conducted two unlinked, anonymous cross-sectional surveys among all participants of the 2022 and 2024 International Conferences on Deprescribing (376 unique email addresses). The first survey addressed pharmaceutical companies; the second focused on MedTech. Quantitative data were analysed descriptively. Free-text responses were analysed thematically. FINDINGS: The response rate was 33% (n = 116/355) for the pharmaceutical survey and 20% (n = 68/335) for the MedTech survey. Trust in deprescribing information was low for pharmaceutical companies, with 52% reporting distrust (n = 47/91). Trust was somewhat higher for MedTech companies, with 27% expressing distrust (n = 14/52). Forty-eight percent (n = 42/87) said they would be less likely to attend an international deprescribing conference with pharmaceutical sponsorship versus 26% (n = 13/49) for MedTech. Among clinicians and clinician-scientists who completed the survey, 27% (n = 18/67) said pharmaceutical companies, and 27% (n = 10/37) said MedTech companies, somewhat or very much influence their deprescribing decisions. CONCLUSIONS: Despite broad scepticism about private-sector involvement and sponsorship of deprescribing activities, views varied.
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How this classification was reachedexpand
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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.006 |
| 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 teacher head, 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".