Overview of impaired facial affect recognition in persons with traumatic brain injury
Bibliographic record
Abstract
PRIMARY OBJECTIVE: To review the literature of affect recognition for persons with traumatic brain injury (TBI). It is suggested that impairment of affect recognition could be a significant problem for the TBI population and treatment strategies are recommended based on research for persons with autism. MAIN OUTCOMES AND RESULTS: Research demonstrates that persons with TBI often have difficulty determining emotion from facial expressions. Studies show that poor interpersonal skills, which are associated with impaired affect recognition, are linked to a variety of negative outcomes. Theories suggest that facial affect recognition is achieved by interpreting important facial features and processing one's own emotions. These skills are often affected by TBI, depending on the areas damaged. Affect recognition impairments have also been identified in persons with autism. Successful interventions have already been developed for the autism population. Comparable neuroanatomical and behavioural findings between TBI and autism suggest that treatment approaches for autism may also benefit those with TBI. CONCLUSIONS: Impaired facial affect recognition appears to be a significant problem for persons with TBI. Theories of affect recognition, strategies used in autism and teaching techniques commonly used in TBI need to be considered when developing treatments to improve affect recognition in persons with brain injury.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".