A Novel Method of Setting Performance Standards in Surgery Using Patient Outcomes
Bibliographic record
Abstract
OBJECTIVE: To describe a novel, outcome-based method of standard setting that differentiates between clinical outcomes rather than arbitrary educational goals. BACKGROUND: Standard setting methods used in assessments of procedural skill are currently not evidence-driven or outcome-based. This represents a potential obstacle for the broad implementation of these evaluations in summative assessments such as certification and credentialing. METHODS: The concept is based on deriving a receiver operating characteristic curve from a regression model that incorporates measures of intraoperative surgeon performance and confounding patient characteristics. This allows the creation of a performance standard that best predicts a clinically significant outcome of interest. The discovery cohort used to create the predictive model was derived from pilot data that used the Global Evaluative Assessment of Robotic Skill assessment tool to predict patient urinary continence 3 months following robotic-assisted radical prostatectomy. RESULTS: A receiver operating characteristic curve with an area under the curve of 0.75 was created from predicted probability statistic generated by the predictive model. We chose a predicted probability of 0.35, based on an optimal tradeoff in sensitivity and specificity (Youden Index). Rearranging the regression equation, we determined the performance score required to predict a 35%, patient-adjusted probability of postoperative urinary incontinence. CONCLUSIONS: This novel methodology is context, patient, and assessment-specific. Current standard setting methods do not account for the heterogeneity of the clinical environment. Workplace-based assessments in competency-based medical education require standards that are credible to the educator and the trainee. High-stakes assessments must ensure that surgeons have been evaluated to a standard that prioritizes satisfactory patient outcomes and 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 | 0.039 | 0.155 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".