Validation of the Rivermead Post-Concussion Symptoms Questionnaire (RPQ) on Patients Injured in High Impact Car Accidents
Bibliographic record
Abstract
Background: The Rivermead Post-Concussion Symptoms Questionnaire (RPQ) is used widely in clinical assessments.Its 16 items describe subjective neuropsychological symptoms.This study evaluates the criterion validity, convergent validity, and internal consistency of the RPQ in a sample of survivors of high impact motor vehicle accidents (MVAs).Method: De-identified data on 65 post-MVA patients (mean age 38.1 years, SD=13.1;24 men, 41 women) were available.Their data include scores on the Rivermead Post-Concussion Symptoms Questionnaire (RPQ), Subjective Neuropsychological Symptoms Scale (SNPSS), Insomnia Severity Index (ISI), Whetstone's and Steiner's measures of post-MVA driving anxiety, and the PCL-5 measure of PTSD.The data also included ratings of the worst pain, least pain, and of average pain (Items 3, 4, and 5 of the Brief Pain Inventory) and ratings of depression, anger, and of anxiety (Items 10 to 12 of the Whiplash Disability Questionnaire).Results: The patients' average RPQ score was 45.5 (SD=9.8)and that of the normal controls 8.3 (13.2): the effect size corresponds to point biserial coefficient of .84,thus indicating a very satisfactory criterion validity.The convergent validity is also satisfactory (r=.79 to the SNPSS).Cronbach alpha coefficient for the full 16 item RPQ was excellent (.97) and would not be improved by evaluating separately the first 3 RPQ items and the next 13 items. Discussion and Conclusions:We recommend that the RPQ be employed jointly with SNPSS in clinical assessments and research.The SNPSS includes important post-concussive symptoms missing in the RPQ as well as other subjective neuropsychological 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 | 0.004 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".