Cluster-based analysis of PTSD-Checklist for DSM-5 (PCL-5) in civilians with post-concussive cognitive changes
Bibliographic record
Abstract
OBJECTIVE: This study explores the relationship between PTSD symptoms and cognition in patients with persistent post-concussive symptoms (PPCS). METHODS: Adults with PPCS presenting to a specialized brain injury clinic provided demographic and injury information and completed the PTSD checklist for DSM-5, Generalized Anxiety Disorder Scale-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9). The Montreal Cognitive Assessment (MoCA) was used to screen for possible cognitive concerns. Multiple regression analysis (MLR) adjusting for age, sex, mechanism of injury, psychiatric history, number of previous concussions, months since most recent injury, and mental health questionnaire scores was used to determine associations between PTSD and cognition. Binomial logistic regression explored the relationship between domains of the MoCA and PCL-5 scores. RESULTS: = 0.021) in patients with PPCS and collinearity of PCL-5 and PHQ-9 scores. Significantly higher Arousal and Reactivity cluster scores within the PCL-5 were associated with poorer scores on naming and abstract tasks on the MoCA. CONCLUSION: The association between specific PCL-5 clusters and lower MoCA scores may represent a viable target for psychotherapeutic and psychopharmacologic intervention in patients with cognitive changes associated with PPCS.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".