Ethics, economics and the regulation and adoption of new medical devices: case studies in pelvic floor surgery
Bibliographic record
Abstract
BACKGROUND: Concern has been growing in the academic literature and popular media about the licensing, introduction and adoption of surgical devices before full effectiveness and safety evidence is available to inform clinical practice. Our research will seek empirical survey evidence about the roles, responsibilities, and information and policy needs of the key stakeholders in the introduction into clinical practice of new surgical devices for pelvic floor surgery, in terms of the underlying ethical principals involved in the economic decision-making process, using the example of pelvic floor procedures. METHODS/DESIGN: Our study involves three linked case studies using, as examples, selected pelvic floor surgery devices representing Health Canada device safety risk classes: low, medium and high risk. Data collection will focus on stakeholder roles and responsibilities, information and policy needs, and perceptions of those of other key stakeholders, in seeking and using evidence about new surgical devices when licensing and adopting them into practice. For each class of device, interviews will be used to seek the opinions of stakeholders. The following stakeholders and ethical and economic principles provide the theoretical framework for the study: Stakeholders--federal regulatory body, device manufacturers, clinicians, patients, health care institutions, provincial health departments, and professional societies. Clinical settings in two centres (in different provinces) will be included. Ethics--beneficence, non-maleficence, autonomy, justice. Economics--scarcity of resources, choices, opportunity costs.For each class of device, responses will be analysed to compare and contrast between stakeholders. Applied ethics and economic theory, analysis and critical interpretation will be used to further illuminate the case study material. DISCUSSION: The significance of our research in this new area of ethics will lie in providing recommendations for regulatory bodies, device manufacturers, clinicians, health care institutions, policy makers and professional societies, to ensure surgical patients receive sufficient information before providing consent for pelvic floor surgery. In addition, we shall provide a wealth of information for future study in other areas of surgery and clinical management, and provide suggestions for changes to health policy.
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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 | 0.036 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| 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".