3.16 Establishing the minimal clinically important difference (MCID) for the adult version of the sport concussion
Bibliographic record
Abstract
Objective This study aimed to determine the responsiveness of the SCAT5 symptom evaluation checklist, and to estimate the minimal clinically important differences for each of the 22 symptoms. Design Cross sectional study. Setting Single Site – Primary Care Setting. Participants A total of 125 subjects were included (72 males, 53 females mean age 18.64 ± 8.66). All subjects were 13 years of age or older and had been diagnosed with a concussion by a primary care physician practicing in sport and exercise medicine. Interventions The SCAT5 symptom evaluation was administered to patients as a component of their routine clinical encounter and the presence and severity (measured on a 7-point Likert scale from 0 to 6) of each of the 22 symptoms was included in the analysis. Outcome Measures Minimal Clinically Important difference (MCID), Minimal Detectable Change (MDC) and Standardized Response Mean (SRM) 22 symptoms, total symptom score and total number of symptoms endorsed. Main Results Overall, the SCAT5 symptom evaluation was highly responsive, with all of the SRM estimates displaying a large effect. The magnitude of the SRM estimates suggests that the SCAT5 symptom evaluation is sensitive to changes in concussion symptom severity. The MDC estimates for all 22 symptoms, the total symptom score and the total number of symptoms endorsed were all lower than the associated MCID estimates therefore the MCID estimates are a true representation of clinical change. Conclusions This study provides a new tool to assist clinicians in the management of concussion and can assist with determining when a patient has a true change in health status. As the nature of the pathology requires the subjective disclosure of symptoms by the patient, the MCID estimate allows clinicians to better interpret the symptom scores and strengthens the return to work, play and learn decisions.
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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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 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) | 0.003 | 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".