3.13 Responsiveness of the post-concussion symptoms scale to monitor clinical recovery following concussion
Bibliographic record
Abstract
Objective To evaluate the responsiveness to change and longitudinal validity of the Post-Concussion Symptom Scale (PCSS) in patients with persistent post-concussive symptoms (PCS). Responsiveness of other clinical outcome measures used to monitor clinical recovery was also explored. Design Prospective cohort clinimetric study. Setting Online questionnaires were collected by a blinded evaluator, and interventions were performed at an interdisciplinary rehabilitation concussion clinic. Participants 109 patients with persistent PCS (between 3 and 12 weeks after the injury) were evaluated at baseline and 6 weeks after a rehabilitation program. Interventions (or Assessment of Risk Factors) A 6-week program including individualized symptom-limited aerobic exercise program combined with education. Outcome Measures Questionnaires included PCSS, Neck Disability Index (NDI), Headache Disability Inventory (HDI), Dizziness Handicap Inventory (DHI), and neck pain and headache Numerical Pain Rating Scales (NPRS). Internal responsiveness was evaluated using Effect Size (ES) and Standardized Response Mean (SRM). External responsiveness was determined with the Minimal Clinical Important Difference (MCID). Pearson correlations were used to determine the longitudinal validity. Main Results PCSS is highly responsive (ES and SRM > 1.3) and has a MCID of 26.5/132 for total score and 5.5/22 for number of symptoms. Low to moderate correlations were found between changes in PCSS and changes in NDI, HDI and DHI. NDI, HDI, DHI and NPRS are also highly responsive (ES and SRM > 0.8). Conclusions All questionnaires including the PCSS are highly responsive and can be used with confidence by clinicians and researchers to evaluate change over time in a concussion population with persistent symptoms.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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) | 0.003 | 0.001 |
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".