Device representatives in hospitals: are commercial imperatives driving clinical decision-making?
Bibliographic record
Abstract
Despite concerns about the relationships between health professionals and the medical device industry, the issue has received relatively little attention. Prevalence data are lacking; however, qualitative and survey research suggest device industry representatives, who are commonly present in clinical settings, play a key role in these relationships. Representatives, who are technical product specialists and not necessarily medically trained, may attend surgeries on a daily basis and be available to health professionals 24 hours a day, 7 days a week, to provide advice. However, device representatives have a dual role: functioning as commissioned sales representatives at the same time as providing advice on approaches to treatment. This duality raises the concern that clinical decision-making may be unduly influenced by commercial imperatives. In this paper, we identify three key ethical concerns raised by the relationship between device representatives and health professionals: (1) impacts on healthcare costs, (2) the outsourcing of expertise and (3) issues of accountability and informed consent. These ethical concerns can be addressed in part through clarifying the boundary between the support and sales aspects of the roles of device representatives and developing clear guidelines for device representatives providing support in clinical spaces. We suggest several policy options including hospital provision of expert support, formalising clinician conduct to eschew receipt of meals and payments from industry and establishing device registries.
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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 | 0.200 | 0.378 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.040 |
| Scholarly communication | 0.025 | 0.016 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.017 | 0.017 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".