Associations between illness perceptions, distress, self-reported cognitive difficulties and cognitive performance after mild traumatic brain injury
Bibliographic record
Abstract
Objectives To examine associations between subjective and objective cognitive problems, and factors potentially modifying these relationships, after mild traumatic brain injury (mTBI). Methods Treatment-seeking adults (n =95) were assessed 6 weeks (Time 1) and then 6months later (Time 2) after mTBI. Validated questionnaires assessed cognitive, emotional and somatic mTBI symptoms, distress, catastrophising, and beliefs about symptoms and recovery. Cognitive performance was measured using the National Institutes of Health Toolbox Cognition Battery. Using correlations and linear regression, we explored associations between psychological factors, objectively measured cognitive performance and self-reported cognitive symptoms. Results There were only modest correlations between subjective cognitive symptoms and objective cognitive performance at assessment timepoints. In contrast, there were medium to large correlations between subjective cognitive symptoms, post-concussion symptom burden and psychological factors. Post-concussion symptom burden and beliefs about symptoms and recovery at Time 1 predicted persisting self-reported cognitive symptoms at Time 2. Conclusions High post-concussion symptom burden and non-recovery expectations may increase risk for persistent subjectively experienced cognitive symptoms. Our findings may guide targeted treatment efforts focusing on factors with potential to influence cognitive symptom reporting after mTBI. Summary In this study, we investigated factors that might influence cognitive recovery after concussion. We found having many post-concussion symptoms and fears of nonrecovery may increase risk for persisting cognitive symptoms. Our findings may guide targeted treatment efforts focusing on factors with potential to influence cognitive symptom reporting after concussion.
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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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".