“First, Do No Harm”
Bibliographic record
Abstract
PURPOSE: To explore surgeons' perceptions of the factors that influence their intraoperative decision making, and implications for professional self-regulation and patient safety. METHOD: Semistructured interviews were conducted with 39 academic surgeons from various specialties at four hospitals associated with the University of Toronto Faculty of Medicine. Purposive and theoretical sampling was performed until saturation was achieved. Thematic analysis of the transcripts was conducted using a constructivist grounded-theory approach and was iteratively elaborated and refined as data collection progressed. A preexisting theoretical professionalism framework was particularly useful in describing the emergent themes; thus, the analysis was both inductive and deductive. RESULTS: Several factors that surgeons described as influencing their decision making are widely accepted ("avowed," or in patients' best interests). Some are considered reasonable for managing multiple priorities external to the patient but are not discussed openly ("unavowed," e.g., teaching pressures). Others are actively denied and consider the surgeon's best interests rather than the patient's ("disavowed," e.g., reputation). Surgeons acknowledged tension in balancing avowed factors with unavowed and disavowed factors; when directly asked, they found it difficult to acknowledge that unavowed and disavowed factors could lead to patient harm. CONCLUSIONS: Some factors that are not directly related to the patient enter into surgeons' intraoperative decision making. Although these are probably reasonable to consider within "real-world" practice, they are not sanctioned in current patient care constructs or taught to trainees. Acknowledging unavowed and disavowed factors as sources of pressure in practice may foster critical self-reflection and transparency when discussing surgical errors.
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.
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 | 0.014 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".