Meta-analysis of facial affect recognition difficulties after traumatic brain injury.
Bibliographic record
Abstract
OBJECTIVE: Difficulties in communication and social relationships present a formidable challenge for many people after traumatic brain injury (TBI). These difficulties are likely to be partially attributable to problems with emotion perception. Mounting evidence shows facial affect recognition to be particularly difficult after TBI. However, no attempt has been made to systematically estimate the magnitude of this problem or the frequency with which it occurs. METHOD: A meta-analysis is presented examining the magnitude of facial affect recognition difficulties after TBI. From this, the frequency of these impairments in the TBI population is estimated. Effect sizes were calculated from 13 studies that compared adults with moderate to severe TBI to matched healthy controls on static measures of facial affect recognition. RESULTS: The studies collectively presented data from 296 adults with TBI and 296 matched controls. The overall weighted mean effect size for the 13 studies was -1.11, indicating people with TBI on average perform about 1.1 SD below healthy peers on measures of facial affect recognition. Based on estimation of the TBI population standard deviation and modeling of likely distribution shape, it is estimated that between 13% and 39% of people with moderate to severe TBI may have significant difficulties with facial affect recognition, depending on the cut-off criterion used. CONCLUSION: This is clearly an area that warrants attention, particularly examining techniques for the rehabilitation of these deficits.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.023 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".