A Randomized Controlled Trial of Emotion Recognition Training After Traumatic Brain Injury
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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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".