3.15 Measurement properties of the adult version of the sport concussion assessment tool 5th edition symptom evaluation using Rasch analysis
Bibliographic record
Abstract
Objective To evaluate the psychometric and measurement properties of the 5th edition of the Sport Concussion Assessment Tool (SCAT5) Symptom Evaluation using Rasch analysis. Design Cross sectional study using Rasch analysis. Setting Single Site – Primary Care Setting. Participants A total of 284 participants who were still experiencing concussion symptoms were included (130 males, 154 females, mean age 20.8 ±10.4). Participants were 13 years of age or older, with a diagnosis of concussion from a primary care physician. Interventions The SCAT5 symptom evaluation was administered to patients as a component of their routine clinical encounter and the presence and severity of each of the 22 symptoms was included in the analysis. Outcome Measures Rasch analysis was performed using RUMM 2030 to assess the SCAT5 symptom evaluation for overall fit, response scaling, individual item fit, differential item functioning, local dependency, unidimensionality and reliability. Main Results The SCAT5 symptom evaluation demonstrated an acceptable fit to the Rasch model, exhibited high reliability and was able to differentiate between at least 4 levels of patients. Nonetheless, serious psychometric issues were identified. Response dependencies were identified between 15 pairs of items. Further, 11 items were found to have sex-linked response biases, and the overall scale was found to be multidimensional (suggesting that the scale is measuring multiple constructs). Conclusions The Rasch model appears unsuitable for psychometric evaluation of the SCAT5 symptom checklist. Methods for addressing these issues will be discussed in the context of leveraging this analysis to create a more reliable and valid tool.
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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 | 0.033 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".