ASSESSMENT FRAMEWORK TO MEASURE ORTHOPAEDIC SURGICAL TEAM PERFORMANCE
Bibliographic record
Abstract
It is well-established that surgeon technical skill performance is a key factor in the success of operative interventions, but there has been less emphasis on evaluating the impact of performance of the entire surgical team. To better understand how to train expert surgical teams and reduce variation in patient outcomes, there is a need for assessment tools that capture strengths, weaknesses, and variation in non-technical skill performance of surgical teams. This study aimed to develop an assessment framework that captured variation in intraoperative teamwork performance of orthopaedic surgical teams. The assessment tools were modified from the Observational Teamwork Assessment for Surgery (OTAS) and included the teamwork constructs of communication, collaboration, coordination, leadership, and monitoring. Assessment tools were developed for four surgical sub-teams: surgery, nursing, radiation technology, and anesthesia. To establish content evidence and modify the tools to suit our context, the study team met with experts from each surgical sub-team and observed cases both in-person and via video. Expert raters from each sub-team assessed teamwork performance from recordings of hip fracture cases for five months. Assessments were conducted using audio, video, intraoperative imaging, and patient monitoring data captured using OR Black Box technology (Surgical Safety Technologies, Toronto, Canada). The study team modified the assessment tools based on expert rater feedback on the content, language, and usability of the tools. The assessment tools include task-specific checklists and global ratings of the five teamwork constructs for each sub-team. Checklist scores across all teamwork constructs for the surgical, nursing, radiation technology, and anesthesia sub-teams were 78% (SD = 35), 43% (SD = 30), 71% (SD = 36), and 65% (SD = 25), respectively. Average global ratings (on a 3-point scale) across all teamwork constructs for the surgical, nursing, and radiation technology sub-teams were 2.00 (SD = 0.00), 2.01 (SD = 0.62), 2.36 (SD = 0.56), and 2.66 (SD = 0.49), respectively. The standard deviations for the average checklist and global rating scores indicate variation in teamwork performance for each sub-team for all but one measure (global ratings for the surgical team). Finding variation in performance is important, as it suggests that our assessments are capturing meaningful differences in teamwork that could result in varied outcomes for patients. Ongoing validation of the assessment framework will include analyzing the internal structure of the assessment tools and exploring how the scores relate to other measures of team performance. Future work will use the assessment framework to identify aspects of team performance that impact outcomes for hip fracture patients to inform the development and training of expert surgical teams.
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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 | 0.037 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".