Reliability of Regression-Based Normative Data for the Oral Symbol Digit Modalities Test: An Evaluation of Demographic Influences, Construct Validity, and Impairment Classification Rates in Multiple Sclerosis Samples
Bibliographic record
Abstract
The oral Symbol Digit Modalities Test (SDMT) has been recommended to assess cognition for multiple sclerosis (MS) patients. However, the lack of adequate normative data has limited its clinical utility. Recently published regression-based norms may resolve this limitation but, because these norms were derived from a relatively small sample, their stability is unclear. We aimed to evaluate the stability of regression-based SDMT norms by comparing existing norms to a cross-validation dataset. First, regression-based normative data were created from a similarly-sized, independent, control sample (n = 94). Next the original and cross-validation norms were compared for equivalency, management of demographic influences, construct validity, and impairment classification rates in a mildly affected MS sample (n = 70). Lastly, similar comparisons were made for a large, representative MS clinic sample (n = 354). We found construct validity and management of demographic influences were equivalent for the two sets of regression-based norms but lower T-scores were obtained using the original dataset, resulting in discrepancies in impairment classification. In conclusion, regression-based norms for the oral SDMT attenuate demographic influences and possess adequate construct validity. However, norms generated using small samples may yield unreliable classification of cognitive impairment. Larger, representative databases will be necessary to improve the clinical utility of regression-based norms.
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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 | 0.042 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 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".