Sex-Related Differences in Emotion Recognition in Multi-concussed Athletes
Bibliographic record
Abstract
OBJECTIVES: Concussion is defined as a complex pathophysiological process affecting the brain. Although the cumulative and long-term effects of multiple concussions are now well documented on cognitive and motor function, little is known about their effects on emotion recognition. Recent studies have suggested that concussion can result in emotional sequelae, particularly in females and multi-concussed athletes. The objective of this study was to investigate sex-related differences in emotion recognition in asymptomatic male and female multi-concussed athletes. METHODS: We tested 28 control athletes (15 males) and 22 multi-concussed athletes (10 males) more than a year since the last concussion. Participants completed the Post-Concussion Symptom Scale, the Beck Depression Inventory-II, the Beck Anxiety Inventory, a neuropsychological test battery and a morphed emotion recognition task. Pictures of a male face expressing basic emotions (anger, disgust, fear, happiness, sadness, surprise) morphed with another emotion were randomly presented. After each face presentation, participants were asked to indicate the emotion expressed by the face. RESULTS: Results revealed significant sex by group interactions in accuracy and intensity threshold for negative emotions, together with significant main effects of emotion and group. CONCLUSIONS: Male concussed athletes were significantly impaired in recognizing negative emotions and needed more emotional intensity to correctly identify these emotions, compared to same-sex controls. In contrast, female concussed athletes performed similarly to same-sex controls. These findings suggest that sex significantly modulates concussion effects on emotional facial expression recognition. (JINS, 2017, 23, 65-77).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".