When medical trainees encountering a performance difficulty: evidence from pupillary responses
Bibliographic record
Abstract
BACKGROUND: Medical trainees are required to learn many procedures following instructions to improve their skills. This study aims to investigate the pupillary response of trainees when they encounter moment of performance difficulty (MPD) during skill learning. Detecting the moment of performance difficulty is essential for educators to assist trainees when they need it. METHODS: Eye motions were recorded while trainees practiced the thoracostomy procedure in the simulation model. To make pupillary data comparable among trainees, we proposed the adjusted pupil size (APS) normalizing pupil dilation for each trainee in their entire procedure. APS variables including APS, maxAPS, minAPS, meanAPS, medianAPS, and max interval indices were compared between easy and difficult subtasks; the APSs were compared among the three different performance situations, the moment of normal performance (MNP), MPD, and moment of seeking help (MSH). RESULTS: The mixed ANOVA revealed that the adjusted pupil size variables, such as the maxAPS, the minAPS, the meanAPS, and the medianAPS, had significant differences between performance situations. Compared to MPD and MNP, pupil size was reduced during MSH. Trainees displayed a smaller accumulative frequency of APS during difficult subtask when compared to easy subtasks. CONCLUSIONS: Results from this project suggest that pupil responses can be a good behavioral indicator. This study is a part of our research aiming to create an artificial intelligent system for medical trainees with automatic detection of their performance difficulty and delivering instructional messages using augmented reality technology.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".