Challenges and potential solutions to the evaluation, monitoring, and regulation of surgical innovations
Bibliographic record
Abstract
BACKGROUND: As it may be argued that many surgical interventions provide obvious patient benefits, formal, staged assessment of the efficacy and safety of surgical procedures has historically been and remains uncommon. The majority of innovative surgical procedures have therefore often been developed based on anatomical and pathophysiological principles in an attempt to better manage clinical problems. MAIN BODY: In this manuscript, we sought to review and contrast the models for pharmaceutical and surgical innovation in North America, including their stages of development and methods of evaluation, monitoring, and regulation. We also aimed to review the present structure of academic surgery, the role of methodological experts and funding in conducting surgical research, and the current system of regulation of innovative surgical procedures. Finally, we highlight the influence that evidence and surgical history, education, training, and culture have on elective and emergency surgical decision-making. The above discussion is used to support the argument that the model used for assessment of innovative pharmaceuticals cannot be applied to that for evaluating surgical innovations. It is also used to support our position that although the evaluation and monitoring of innovative surgical procedures requires a rigorous, fit-for-purpose, and formal system of assessment to protect patient safety and prevent unexpected adverse health outcomes, it will only succeed if it is supported and championed by surgical practice leaders and respects surgical history, education, training, and culture. CONCLUSION: We conclude the above debate by providing a recommended approach to the evaluation, monitoring, and regulation of surgical innovations, which we hope may be used as a guide for all stakeholders involved in interpreting and/or conducting future surgical research.
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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 | 0.549 | 0.584 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.012 | 0.081 |
| Scholarly communication | 0.047 | 0.053 |
| Open science | 0.017 | 0.026 |
| Research integrity | 0.039 | 0.041 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".