Correlation of Laparoscopic Experience With Differential Functional Brain Activation
Bibliographic record
Abstract
HYPOTHESIS: Regions of functional brain activation differ between novice and expert laparoscopists. DESIGN: We compared novice and expert laparoscopists using positron emission tomography (PET) during the peg transfer task of the McGill Inanimate System for Training and Evaluation of Laparoscopic Skills (MISTELS) protocol. The first scan (rest) was performed with the subject's eyes closed. The second scan (video 1) was performed while watching a peg transfer video. The third scan (peg 1) was acquired during the peg transfer task. The forth scan (peg 2) was performed after practicing 15 minutes. The fifth scan (video 2) was performed after peg 2 as the subject watched a video clip of a laparoscopic partial nephrectomy. The sixth scan (peg 3) was conducted during the final peg transfer task after 15 more minutes of practice. SETTING: Feinstein Institute for Medical Research. PARTICIPANTS: Five novice and 5 expert laparoscopists. MAIN OUTCOME MEASURES: Differences in brain activation as determined by changes in regional cerebral blood flow on PET scans with oxygen 15-labeled water. RESULTS: The first analysis examined group differences between the 3 peg scans and the rest scan. The novice group had a significantly (P < .001) higher activation (with deactivation in the expert group) in the left precentral gyrus and insula and the right precuneus and inferior occipital gyrus. The second analysis compared the 2 video scans and the rest scan. In contrast to the expert group, the novices had significantly (P < .001) higher activation in the right precuneus and cuneus but deactivation in the bilateral posterior cerebellum. CONCLUSIONS: This study demonstrates differential regional brain activation patterns between novice and expert laparoscopists during surgery-related motor and visual association tasks.
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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 | 0.000 | 0.004 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".