Increased Neck Visual Fixation in Children With Tracheostomies: An Eye‐Tracking Study
Bibliographic record
Abstract
OBJECTIVE: The aim of this study is to objectively quantify the attentional distraction effect of a tracheostomy on visual attention and gaze using eye-tracking technology. METHODS: This was a cross-sectional observational study. An eye-tracking device was used to capture eye movements of participants over 5 years of age who were shown 20 images of children with and without tracheostomies. The primary outcome was the number and duration of fixations on different mapped regions of the head and neck, including the central triangle (CT) and peripheral triangle (PT) of the face, and the anterior neck. Facial recall recognition was also assessed and compared across groups. RESULTS: A total of 160 participants were recruited (57% female, median age 18 years, range 6-54 years). The greatest amount of time was spent viewing the CT of both children with and without tracheostomies. Participants viewed the necks of children with tracheotomies 3.7 times more frequently, spent eight times longer viewing the neck, and were more likely to incorrectly recall children with tracheotomies compared with the control group (3.33 vs. 0.85 fixations, p < 0.001; 0.15 vs. 1.12 ms, p < 0.0001; p < 0.001). CONCLUSION: Viewers who are looking at children with tracheostomies redirect their attention from the face toward the neck and spend a longer duration of time viewing the necks of children with tracheostomies compared to those without. Viewers are also more likely to incorrectly recall the faces of children with tracheostomies. These findings highlight a possible explanation for why social interactions may be different for children with tracheostomies beyond their underlying medical and communication problems.
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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 | 0.001 | 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".