Assessing Mastery of Laparoscopic Skills for a Junior Resident Prior to Entering the Operating Room
Bibliographic record
Abstract
Background: Simulation has become a standard for developing laparoscopic skills. Common training programs base their passing scores on comparing noncompetent to competent surgeons (Fraser et al., 2003; Thinggaard et al., 2015). With the era of competency-based medical education (CBME), passing scores should be based on the expected skills from the resident for the next stage of training. Purpose: The purpose of this study was to develop a passing score for the CanTAB test which is reflective of the skill expected of a “well-prepared” obs/gyn resident about to start assisting in the OR. The CanTAB test is a modified version of the Training and Assessment of Basic Laparoscopic Techniques (TABLT) test using supplies available in Canada (Thinggaard et al., 2014/2018, 2015). The “well-prepared” resident was operationalized as a resident who could follow explicit instructions, a 2.0 on the Ottawa Surgical Competency Operating Room Evaluation (OSCORE) (Gofton et al., 2012). Methods: Residents and faculty at the University of Manitoba (UofM) obs/gyn department were recruited. Participants could complete any or all of the following: an OR assessment based on the OSCORE and Global Objective Assessment of Laparoscopic Skills (GOALS) tools, the CanTAB test, and/or the mastery Angoff standard-setting exercises informed by a participant’s individual results or the cumulative study results. CanTAB passing scores were calculated using two standard-setting methods: The mastery Angoff and “well-prepared” borderline regression. Results: Eleven participants completed the study: 6 residents and 5 fellows/attendings. The CanTAB scores ranged from 723(123.5) to 1625(503.4), with senior residents scoring the highest. The CanTAB passing scores were 602 for mastery Angoff-1, 862 for mastery Angoff-2, 993 (p=0.005) for the “well-prepared” regression line between the OSCORE and CanTAB scores, and 1270 (p=0.469) for the “well-prepared” regression line between the GOALS and CanTAB scores. The passing rates ranged from 25 to 100%, with the OSCORE-based regression line having the second-lowest passing rate with 25% of junior residents and 100% of senior residents and attendings passing. Conclusion: A CanTAB passing score of 993, based on the OSCORE level of 2.0 well-prepared borderline regression method, can best identify resident well-prepared to assist laparoscopic surgery with explicit instructions in the OR.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".