Can Sport Concussion Assessment Tool (SCAT) Symptom Scores Be Converted to Rivermead Post-concussion Symptoms Questionnaire (RPQ) Scores and Vice Versa? Findings From the Toronto Concussion Study
Bibliographic record
Abstract
Background: The Rivermead Post-Concussion Symptoms Questionnaire (RPQ) and the Sports Concussion Assessment Tool (SCAT) are widely used self-report tools assessing the type, number, and severity of concussion symptoms. There are overlapping symptoms and domains, though they are scored differently. The SCAT consists of 22 questions with a 7-point Likert scale for a total possible score 132. The RPQ has 16 questions and a 5-point Likert scale for a total of 64 possible points. Being able to convert between the two scores would facilitate comparison of results in the concussion literature. Objectives: To develop equations to convert scores on the SCAT to the RPQ and vice versa. Methods: Adults (17–85 years) diagnosed with a concussion at a referring emergency department were seen in the Hull-Ellis Concussion and Research Clinic, a rapid access concussion clinic at Toronto Rehab–University Health Network (UHN) Toronto Canada, within 7 days of injury. The RPQ and SCAT symptom checklists as well as demographic questionnaires were administered to all participants at Weeks 1, 2, 3, 4, 5, 6, 7, 8, 12, 16. Results: 215 participants had 1,168 matched RPQ and SCAT assessments. Total scores of the RPQ and the SCAT had a rho = 0.91 (p < 0.001); correlations were lower for sub-scores of specific symptom domains (range 0.74–0.87, p < 0.001 for all domain comparisons). An equation was derived to calculate SCAT scores using the number and severity of symptoms on the RPQ. Estimated scores were within 3 points of the observed total score on the SCAT. A second equation was derived to calculate the RPQ from the proportion weighted total score of the SCAT. This equation estimated corresponding scores within 3 points of the observed score on the RPQ. Conclusions: The RPQ and SCAT symptom checklists total scores are highly correlated and can be used to estimate the total score on the corresponding assessment. The symptom subdomains are also strongly correlated between the 2 scales however not as strongly correlated as the total score. The equations will enable researchers and clinicians to quickly convert between the scales and to directly compare concussion research findings.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".