“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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.008 | 0.009 |
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; both teacher heads agree on what is shown here.
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".