954 A Framework Recommendation for Surgeons Legal Liabilities During an Acute Health Crisis
Bibliographic record
Abstract
Abstract Introduction Ethical and professional duties compelled surgeons to act outside their specialties during the SARS-CoV-2 pandemic. Our work explored the legal liabilities that have arisen for surgeons during this period. Method A literature review was conducted of medical and legal databases; PubMed, MedlineOvid, WestLaw, and LexisNexis. Statues and case law across three jurisdictions; Canada, the United Kingdom, and the Republic of Singapore were retrieved and analyzed. Results Professional regulatory bodies impose a duty to act in healthcare emergencies. Yet, formal legal protection has not been codified by either professional regulatory bodies or governments. These discrepancies between legal and professional standards leave surgeons vulnerable to litigation. Conclusions Following our analysis of cases and outcomes within these jurisdictions we propose a framework that provides basic protection to surgeons acting outside their surgical specialty, but within a general medical capacity, whilst providing care during acute health crises.
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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 | 0.051 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.021 | 0.007 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.020 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".