A Randomized Controlled Trial of Emotion Recognition Training After Traumatic Brain Injury
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To examine the effectiveness of 2 affect recognition interventions (Faces and Stories) in people with a traumatic brain injury. SETTING: Postacute rehabilitation facilities. PARTICIPANTS: A total of 203 participants with moderate to severe traumatic brain injury were screened; 71 were eligible and randomized to the Faces (n = 24), Stories (n = 23), and Control interventions (n = 24). Participants were an average of 39.8 years of age and 10.3 years postinjury; 74% of participants were male. DESIGN: Randomized controlled trial with immediate, 3-month, and 6-month follow-up posttests. Interventions were 9 hours of computer-based training with a therapist. MEASURES: Diagnostic Assessment of Nonverbal Accuracy 2-Adult Faces; Emotional Inference From Stories Test; Empathy (Interpersonal Reactivity Index); and Irritability and Aggression (Neuropsychiatric Inventory). RESULTS: The Faces Intervention did significantly better than the Control Intervention on the Diagnostic Assessment of Nonverbal Accuracy 2-Adult Faces (P = .031) posttreatment; no time effect or group interaction was observed. No other significant differences were noted for the Faces Intervention. No significant differences were observed between the Stories and the Control Interventions; however, a significant time effect was found for the Emotional Inference From Stories Test. CONCLUSION: The Faces Intervention effectively improved facial affect recognition in participants with chronic post-traumatic brain injury, and changes were maintained for 6 months. Future work should focus on generalizing this skill to functional behaviors.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it