A novel method for assessing visual perception of surgical planes
Bibliographic record
Abstract
BACKGROUND: Recognition of tissue planes during surgery appears to be a skill acquired with experience. We conducted a pilot study to test this hypothesis using a novel method for evaluating this skill in a simulated environment. METHODS: Twelve surgeons of varying levels of experience were shown 16 captured images from a mesorectal excision. For each image, they were asked to draw the ideal dissection plane with a stylus on a tablet computer. We used a novel metric for comparing agreement between lines to determine the level of precision observed between junior and senior trainees and consultant surgeons and measure the accuracy of junior and senior trainees compared with consultant surgeons. RESULTS: We observed significant differences in precision for 9 of 16 images; 7 of these followed the predicted stepwise pattern associated with level of experience. Using consultant surgeons as the reference standard, we observed significant differences in accuracy between senior and junior trainees for 11 images, with senior trainees being more accurate in 10 of them. Only 2 images failed to contribute significant findings to our analysis. CONCLUSION: The findings of this pilot evaluation of a novel method for measuring a surgeon's ability to recognize tissue planes in a simulated model show that skill improves with experience. Further evaluation of this method will reveal its utility as an assessment tool and possibly as a training instrument.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".