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Record W4414528107 · doi:10.1080/02699052.2025.2565640

Comparative predictive validity of a psychological risk screening tool in adults after mild traumatic brain injury

2025· article· en· W4414528107 on OpenAlexaff
Deborah L. Snell, Ana Mikolić, Josh W. Faulkner, Alice Theadom, Noah D. Silverberg

Bibliographic record

VenueBrain Injury · 2025
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of British Columbia
FundersHealth Research Council of New Zealand
KeywordsTraumatic brain injuryPredictive validityPredictive valueReferralInjury preventionPredictive value of testsPoison controlPsychological interventionRisk assessment

Abstract

fetched live from OpenAlex

Objective To determine the comparative predictive validity of the Subgroups for Targeted Treatment modified for concussion (STarT-C) with full-length psychological measures (legacy questionnaires) in adults, six months after mild traumatic brain injury (mTBI).Materials and Methods Participants (n = 107) were recruited from outpatient concussion services in New Zealand and assessed on average 6 weeks (Time 1) and 6 months after mTBI (Time 2). The primary outcome was post-concussion symptoms at Time 2 measured with the Rivermead Post-concussion Symptoms Questionnaire (RPQ). Comparative predictive validity was determined by comparing the STarT-C at Time 1 with full-length legacy questionnaires that measured STarT-C constructs (distress, depression, fear avoidance, recovery expectations, catastrophizing) at Time 1.Results The STarT-C total score and psychosocial sub-score showed significant correlations with all psychological legacy questionnaires at Time 1 (r = ~0.3 to ~ 0.7). The STarT-C showed similar additional predictive value on symptoms at Time 2, as all legacy psychological questionnaires together (delta R2 = 8% vs. delta R2 = 8%).Conclusions The STarT-C showed comparable prognostic value for post-concussion symptom outcomes with a battery of psychological questionnaires. Further research should consider if stratified risk using STarT-C high, medium, and low sub-categories improves targeted treatment referral decision making by clinicians and mTBI outcomes.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.135
GPT teacher head0.426
Teacher spread0.291 · 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.

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

Citations1
Published2025
Admission routes1
Has abstractyes

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