TP8.2.9 Development & Evaluation of LapPass™: The Laparoscopic Passport
Bibliographic record
Abstract
Abstract Aims Laparoscopic surgery is technically challenging and assessment of competency is necessary to ensure patient safety and guide training. Existing tools of assessment are mostly subjective, with a growing need for objective credentialing. LapPass™ was developed by a UK-based laparoscopic society as an accessible simulation assessment tool. The aim of this study was to report on its development and preliminary findings of usability and validity. Methods LapPass™ consists of 4 tasks that test: bimanual dexterity, simulated appendicectomy, dissection and intracorporeal suturing. Participants were prospectively recruited from testing events. Online surveys were sent to assessors and participants to assess the usability, face and content validity of the tool. Options to respond were on a five-point Likert scale with ratings from strongly disagree (1) to strongly agree (5). Results LapPass was launched and offered to trainees as free-of-charge assessment tool. 31 participants and 12 assessors took part. The 1st time pass rate for bimanual dexterity was 19/29 (65.5%), appendicectomy 13/23 (56.5%), dissection 20/27 (74.1%) and intracorporeal suturing 6/19 (31.5%). The mean scores for participants’ usability and validity were 3.8 and 4.12 for bimanual dexterity; 3.96 and 4.37 for appendicectomy; 4.5 and 4.16 for dissection and 3.84 and 4.52 for intracorporeal suturing. Assessors' mean score of usability was 4.5 across all tasks. Assessors scored validity of bimanual dexterity 4.35, appendicectomy 4.42, dissection 3.71 and intracorporeal suturing 4.65. Conclusion LapPass™ is a an accessible objective assessment tool for laparoscopic basic surgical skills with preliminary data to confirm its usability and face and content validity.
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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 | 0.003 | 0.001 |
| 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) | 0.001 | 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 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".