Anxiety Sensitivity and Alexithymia as Mediators of Postconcussion Syndrome Following Mild Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVE: To examine the influence of anxiety sensitivity (AS) and alexithymia as potential mediators for the development of psychological distress and postconcussion syndrome after mild traumatic brain injury (mTBI). PARTICIPANTS: Sixty-one patients with mTBI assessed at a mean of 2.38 weeks after injury and demographically matched healthy controls (n = 61). MEASURES: Twenty-item Toronto Alexithymia Scale, Anxiety Sensitivity Index, State-Trait Anxiety Inventory, and Rivermead Post Concussion Questionnaire. RESULTS: The mTBI group reported significantly higher levels of AS, alexithymia, psychological distress, and postconcussion (PC) symptom scores than controls. High AS and alexithymia in the mTBI group were associated with a greater number of PC symptoms and higher levels of psychological distress than patients scoring low on these measures and controls. In the mTBI group, a combination of AS and low mood explained 52.6% of the variance in PC symptom reporting. A combination of trait-anxiety, alexithymia, and PC symptoms explained 77.2% of the variance in levels of mood. CONCLUSION: A combination of low mood and high AS may act as a psychological diathesis for the development of persisting PC symptoms. Early identification could provide a focus for early intervention to prevent the development of postconcussion syndrome after mTBI.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".