Unusual suspects: Real-time physiological evaluation of stressors during laparoscopic donor nephrectomy
Bibliographic record
Abstract
INTRODUCTION: The purpose of this study was to document the variability of faculty surgeon electrodermal activity (EDA) peaks during laparoscopic donor nephrectomy (LDN) to determine the effect of case difficulty and learner expertise on the stress response. METHODS: EDA for a single faculty surgeon was captured over 15 LDN cases using an Empatica E4 wristband. During each case, one of three transplant fellows (novice, intermediate, or expert level LDN expertise) participated. Difficulty was rated preoperatively as "low/moderate/high" by the faculty. EDA peaks were collected and analyzed; the frequency and magnitude of EDA peaks, case difficulty, and fellow expertise were compared using a two-way factorial ANOVA. RESULTS: The main effects of learner expertise (F[2, 308]=11.27, p<0.001) and difficulty rating (F[2, 414]=15.13, p<0.001) were significant. The interaction between difficulty and expertise on faculty EDA peaks was also significant (F[3, 391]=14.29, p<0.001). The novice fellow resulted in higher faculty EDA levels compared to intermediate and expert fellows on low-difficulty cases, but not moderate- or high-difficulty cases. CONCLUSIONS: This is the first report examining faculty surgeon EDA across cases of varying difficulty and varying learner expertise during a high-stakes operation. EDA levels were inversely proportional to the expertise of the learner and case difficulty, suggestive of a significant impact of learner autonomy on faculty stress response.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".