Introducing New Technologies: Protecting Subjects of Surgical Innovation and Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The system for protecting human research subjects is under increasing pressure. Under the currently dominant Regulatory Ethics Paradigm, clinical research protocols must be reviewed and approved by an institutional review board (IRB) or equivalent. Although the IRB was introduced into health care in part to protect patients and investigators from the inherent conflict between the best clinical interest of the individual patient and the interest of science and society in answering a clinical question, its rigorous standards and rigid framework discourage surgeons from seeking potentially valuable early IRB consultation. Most of the important advances in the history of medicine, such as anesthesia, appendectomy, antibiotics, intensive care, and immunization, were introduced through an informal, unregulated innovation process that has been enormously productive but can lead to ratification of ineffective or harmful treatment by credulous physicians and patients. We propose a surgical innovation ethics paradigm that is a more nimble, flexible source of institutional and public oversight and approval of innovations that are in the gray zone prior to their conversion to formal protocols that then require IRB approval. We also discuss the management of personal and institutional conflicts of interest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it