Surgical innovation or surgical evolution: an ethical and practical guide to handling novel neurosurgical procedures
Bibliographic record
Abstract
OBJECT: Surgical innovation is an important driver of improvements in technique and technology, which ultimately translates into improvements in patients' outcomes. Nevertheless, patients may face new risks of morbidity and mortality when surgical innovation is used, and well-intentioned surgical "experimentation" on patients must be regulated and monitored. In this paper the authors examine the challenges of defining surgical innovation and briefly review the literature on this challenging subject. METHODS: Using examples from the field of neurosurgery and in part from the personal experience of the senior author, the authors develop a model of levels of experimental acuity of surgical procedures and offer recommendations on how these procedures would best be regulated. CONCLUSIONS: The authors propose guidelines for determining the need for regulation of innovation. The potential role of institutional review boards in this process is highlighted.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".