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Record W4401180532 · doi:10.1080/02699052.2024.2381065

Cluster-based analysis of PTSD-Checklist for DSM-5 (PCL-5) in civilians with post-concussive cognitive changes

2024· article· en· W4401180532 on OpenAlexaffabout
Erik A. Fraunberger, Alison Janelle Wilson, Adam Idriss, Christina Campbell, Regan King, Meng Wang, Chantel T. Debert

Bibliographic record

VenueBrain Injury · 2024
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsChecklistCognitionPsychologyCluster (spacecraft)Clinical psychologyPsychiatryCognitive psychology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.553
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.360
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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