Assessing Medical Student Attentional Bias in Unicoronal Craniosynostosis: An Eye-Tracking Study
Bibliographic record
Abstract
ObjectiveUsing a well-established measure of attention, we aimed to objectively identify medical students' gaze patterns when assessing children with/without unicoronal craniosynostosis (UCS) and identify potential diagnostic and educational gaps in their assessment.DesignMedical student participants viewed a series of images of children with/without UCS. Eye movements were recorded using a table-mounted eye-tracking device. Dwell times for 8 interest areas (forehead, brow, eyes, nose, lower face, mouth, left ear, right ear) were compared.ParticipantsThirty medical students (21 males, 9 females, mean age = 29.9 years old) were recruited from the local medical school.Main Outcome MeasureThe main outcome measure was the cumulative dwell times (milliseconds) participants spent within the 8 facial regions (forehead, brow, eyes, nose, lower face, mouth, left ear, right ear).ResultsParticipants spent significantly more time on the brow region (P < 0.001) and less on the nose region (P < 0.05) when viewing UCS images. Furthermore, there were significantly longer dwell times to the side of the forehead contralateral to the fused suture in UCS images (P < 0.05). In control images, participants focused more on the right side of the mouth (P < 0.05). No other significant differences in dwell times were observed.ConclusionThis study demonstrates that medical students exhibit attentional biases when viewing children with UCS and focus on only some of the asymmetries seen in UCS. These findings highlight potential educational gaps emphasizing the need for targeted training in craniofacial assessment.
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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 | 0.002 |
| 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 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".