Distress Evokes a Visual Attention Bias to Treatment‐Related Scenes in Children and Adolescents Treated for a Posterior Fossa Brain Tumour
Bibliographic record
Abstract
OBJECTIVE: Distress following treatment is common in children and adolescents treated for a brain tumour, reflecting underlying difficulties with emotion regulation. However, the medical factors that evoke distress remain poorly understood. Cranial radiation therapy (CRT), administered for malignant tumours, is associated with a high treatment burden and poor emotion regulation. This study investigates the effect of CRT on distress compared to non-CRT treatment and typically developing children (TDC), using self-report and eye-tracking measures. METHOD: Data were collected from 18 TDC and 36 children and adolescents treated for a posterior fossa brain tumour, including 17 who received CRT and 19 who did not. Participants completed a questionnaire assessing distress and then free-viewed treatment-related and emotional scenes while their eyes were tracked. Visual avoidance of a scene type served as a behavioural indicator of implicit emotion regulation. RESULTS: Participants treated with CRT reported distress more frequently (84.21%) compared to those treated without CRT (58.82%) and TDC (52.94%). Distressed participants exhibited greater visual avoidance of treatment-related scenes than their non-distressed counterparts. Participants treated with CRT also demonstrated more visual avoidance than those treated without CRT or TDC. CONCLUSION: The high treatment burden associated with CRT likely contributes to increased distress and visual avoidance of treatment-related scenes, which may reflect attempts to regulate unpleasant emotional responses. Accumulating medical procedures may heighten threat sensitivity and reinforce avoidance as a strategy for emotion regulation. Thus, visual avoidance may serve as a behavioural marker of distress and help identify patients who are at an elevated risk.
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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 | 0.003 |
| 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 | 0.000 | 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".