What Are the Principles That Guide Behaviors in the Operating Room?
Bibliographic record
Abstract
OBJECTIVE: To identify the core principles that guide expert intraoperative behaviors and to use these principles to develop a universal framework that defines intraoperative performance. BACKGROUND: Surgical outcomes are associated with intraoperative cognitive skills. Yet, our understanding of factors that control intraoperative judgment and decision-making are limited. As a result, current methods for training and measuring performance are somewhat subjective-more task rather than procedure-oriented-and usually not standardized. They thus provide minimal insight into complex cognitive processes that are fundamental to patient safety. METHODS: Cognitive task analyses for 6 diverse surgical procedures were performed using semistructured interviews and field observations to describe the thoughts, behaviors, and actions that characterize and guide expert performance. Verbal data were transcribed, supplemented with content from published literature, coded, thematically analyzed using grounded-theory by 4 independent reviewers, and synthesized into a list of items. RESULTS: A conceptual framework was developed based on 42 semistructured interviews lasting 45 to 120 minutes, 5 expert panels and 51 field observations involving 35 experts, and 135 sources from the literature. Five domains of intraoperative performance were identified: psychomotor skills, declarative knowledge, advanced cognitive skills, interpersonal skills, and personal resourcefulness. Within the advanced cognitive skills domain, 21 themes were perceived to guide the behaviors of surgeons: 18 for surgical planning and error prevention, and 3 for error/injury recognition, rescue, and recovery. The application of these thought patterns was highly case-specific and variable amongst subspecialties, environments, and individuals. CONCLUSIONS: This study provides a comprehensive definition of intraoperative expertise, with greater insight into the complex cognitive processes that seem to underlie optimal performance. This framework provides trainees and other nonexperts with the necessary information to use in deliberate practice and the creation of effective thought habits that characterize expert performance. It may help to identify gaps in performance, and to isolate root causes of surgical errors with the ultimate goal of improving patient safety.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".