The prognostic value of a screening tool for psychological risk factors after mild traumatic brain injury: prospective studies in Canada and New Zealand
Bibliographic record
Abstract
Objective To investigate the prognostic value of the Subgroups for Targeted Treatment (STarT) Screening Tool adapted for concussion (STarT-C) on persistent symptoms and disability at 6–9 months following mild traumatic brain injury (mTBI). Design Secondary analysis of two prospective studies: an observational cohort study in New Zealand and usual care control arm of a clinical trial in Canada (ClinicalTrials.gov Registry ( NCT04704037 )). Setting Participants in the New Zealand cohort were recruited from concussion clinics (five sites) and those in the Canadian cohort were recruited from emergency departments/urgent care centres (eight sites). Participants New Zealand participants (n=93, median age 37 years, 60% women) were assessed at median=6 weeks post-injury (T1) and 6 months later (T2). Canadian participants (n=223, median age 38 years, 56% women) were assessed at median=2 weeks (T1) and 6 months later (T2). Main outcome measures Symptoms at T2 were assessed using the validated Rivermead Postconcussion Symptoms Questionnaire (RPQ) and disability using the WHO Disability Assessment Schedule 2.0 12-item Interview. Results In linear regression analyses, the STarT-C predicted symptom burden (R2=18–36%) and disability (R2=15–18%) at T2 in both cohorts. While the additional prognostic value over and above baseline variables was substantial (delta R2 8–40%), the additional prognostic value over the RPQ at T1 was variable and generally lower (delta R2=1–9%). Conclusion The STarT-C—a brief screening tool—predicted persistent symptoms and disability in adults following mTBI. The incremental prognostic value of the STarT-C over the RPQ may be variable, but regardless, the tool may be useful for identifying those at risk of prolonged recovery who may benefit from early psychological intervention.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".