QOL-09. EMOTION RECOGNITION IN PEDIATRIC BRAIN TUMOR PATIENTS: VIEWING PATTERNS AND WHITE MATTER STRUCTURE
Bibliographic record
Abstract
Pediatric brain tumor patients display emotion recognition deficits, and eye-movement monitoring might help explain why. Identifying facial emotions is thought to rely on white matter (WM) that connects posterior, limbic and frontal brain regions. Thus, we examined if emotion recognition deficits are related to viewing patterns and to WM. 22 patients treated for posterior fossa (PF) brain tumors and 12 healthy children participated in this study at SickKids (Toronto, Ontario). Participants completed the Diagnostic Analysis of Nonverbal Accuracy (DANVA-2), a computerized task that measures facial emotion recognition using photographs, while their eye-movements were recorded. Diffusion tensor imaging (DTI) was used to assess fractional anisotropy (FA). Whole brain voxel-based analyses were conducted to compare WM between patients and controls. Regional WM was correlated with the number of incorrect responses across all participants. Patients made more emotion recognition errors than controls (p=0.02). However, patients and controls did not differ in the number of fixations made on the photograph: (p=0.21), or in the total time spent looking at the photograph: (p=0.23). Relative to controls, patients had lower FA in many voxels across the brain (all p<0.05). Across all participants, FA was negatively correlated with the number of incorrect responses in the left temporal region (r=-0.503, p=0.005). Patients treated for brain tumors display emotion recognition deficits and WM damage. The emotion recognition deficits do not appear to result from inattention to the photographs. Our results suggest that left temporal WM may be important for successful emotion recognition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.004 | 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".