Facial-affect recognition deficit as a predictor of different aspects of social-communication impairment in traumatic brain injury.
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between facial-affect recognition and different aspects of self- and proxy-reported social-communication impairment following moderate-severe traumatic brain injury (TBI). METHOD: Forty-six adults with chronic TBI (>6 months postinjury) and 42 healthy comparison (HC) adults were administered the La Trobe Communication Questionnaire (LCQ) Self and Other forms to assess different aspects of communication competence and the Emotion Recognition Test (ERT) to measure their ability to recognize facial affects. RESULTS: Individuals with TBI underperformed HC adults in the ERT and self-reported, as well as were reported by close others, as having more communication problems than did HC adults. TBI group ERT scores were significantly and negatively correlated with LCQ-Other (but not LCQ-Self) scores (i.e., participants with lower emotion-recognition scores were rated by close others as having more communication problems). Multivariate regression analysis revealed that adults with higher ERT scores self-reported more problems with disinhibition-impulsivity and partner sensitivity and had fewer other-reported problems with disinhibition-impulsivity and conversational effectiveness. CONCLUSIONS: Our findings support growing evidence that emotion-recognition deficits play a role in specific aspects of social-communication outcomes after TBI and should be considered in treatment planning. (PsycINFO Database Record
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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 | 0.001 | 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) | 0.002 | 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".