6.10 Predictors of post-concussion disability
Bibliographic record
Abstract
Objectives To investigate predictors of self-reported disability (Sheehan Disability Scale (SDS)) using well-validated symptom measures from our clinical dataset (Rivermead Post-Concussion Symptoms Questionnaire (RPQ) and the Sport Concussion Assessment Tool (SCAT5). To determine if the relationship between symptom severity and disability depends on current age or time since injury in weeks (TSIwk). Design This is an observational cross sectional study of patient surveys from the Concussion Ontario Network: Neuroinformatics to Enhance Clinical care and Translation (CONNECT) clinical dataset. Participants Participants for this study (N=198) were included if they met 2017 Berlin Consensus concussion criteria, ≥ 16 years old, negative imaging, proficient in English, no communication difficulties, GCS ≥ 14, and of variable chronicity. Exclusion criteria included abnormality on imaging, neurosurgical operative intervention, intubation or treatment in ICU, multisystem injuries, chronic condition of developmental delay affecting communication, and lack of trauma history as the primary event. Intervention/Outcome Measures The independent variables age, TSIwk, and RPQ total score/SCAT5 conversion (RPQT) were used in a multiple regression as predictors of the outcome measure/dependent variable SDS total score (SDST). Main Results The full regression model indicated that RPQT was a significant predictor of current disability regardless of age or chronicity (Adj. R² = 0.44, df = 3, 194 ; p<.0001; t for age, TWIwk < 0.50, p > 0.62). Conclusions These results support the clinical utility of the RPQ as a current predictor of self-reported disability in adults regardless of age or chronicity of injury.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".