Factors Affecting Surgical Decisionmaking—A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Guidelines and Class 1 evidence are strong factors that help guide surgeons' decision-making, but dilemmas exist in selecting the best surgical option, usually without the benefit of guidelines or Class 1 evidence. A few studies have discussed the variability of surgical treatment options that are currently available, but no study has examined surgeons' views on the influential factors that encourage them to choose one surgical treatment over another. This study examines the influential factors and the thought process that encourage surgeons to make these decisions in such circumstances. METHODS: Semi-structured face-to-face interviews were conducted with 32 senior consultant surgeons, surgical fellows, and senior surgical residents at the University of Toronto teaching hospitals. An e-mail was sent out for volunteers, and interviews were audio-recorded, transcribed verbatim, and subjected to thematic analysis using open and axial coding. RESULTS: Broadly speaking there are five groups of factors affecting surgeons' decision-making: medical condition, information, institutional, patient, and surgeon factors. When information factors such as guidelines and Class 1 evidence are lacking, the other four groups of factors-medical condition, institutional, patient, and surgeon factors (the last-mentioned likely being the most powerful)-play a significant role in guiding surgical decision-making. CONCLUSIONS: This study is the first qualitative study on surgeons' perspectives on the influential factors that help them choose one surgical treatment option over another for their patients.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 teacher head, 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".