Catastrophizing is associated with excess cognitive symptom reporting after mild traumatic brain injury.
Bibliographic record
Abstract
OBJECTIVE: Persistent cognitive symptoms after mild traumatic brain injury (mTBI) often do not correlate with objective neuropsychological performance. Catastrophizing (i.e., excessively negative interpretations of symptoms) may help explain this discrepancy. We hypothesize that symptom catastrophizing will be associated with greater cognitive symptom reporting relative to neuropsychological test performance in people seeking treatment for mTBI. METHOD: = 11.5). Validated questionnaires were used to assess catastrophizing, cognitive symptoms, and affective distress. Neuropsychological performance was assessed using the National Institutes of Health Toolbox Cognition Battery. Discrepancies between cognitive symptoms and cognitive functioning were operationalized using standard residuals from neuropsychological test performance scores regressed on cognitive symptom scores. Generalized linear models were run to measure the association between symptom catastrophizing, cognitive variables, and their discrepancy, with affective distress as a covariate. RESULTS: Symptom catastrophizing was associated with more severe cognitive symptoms when controlling for neuropsychological performance (β = 0.44, 95% CI [0.23, 0.65]). Symptom catastrophizing was also associated with higher subjective-objective cognition residuals (β = 0.43, 95% CI [0.22, 0.64]). Catastrophizing remained a significant predictor after affective distress was introduced as a covariate. CONCLUSIONS: Catastrophizing is associated with misperceptions of cognitive functioning following mTBI, specifically overreporting cognitive symptoms relative to neuropsychological performance. Symptom catastrophizing may be an important determinant of cognitive symptom reporting months after mTBI. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".