Taking the tester out of the SDMT: A proof of concept fully automated approach to assessing processing speed in people with MS
Bibliographic record
Abstract
Background: One factor hindering the widespread use of cognitive testing for people with multiple sclerosis (pwMS) is the need for a tester to administer tests. Objective: To undertake a proof of concept study assessing the feasibility of a fully automated speech recognition version of the Symbol Digit Modalities Test (auto-SDMT) in detecting abnormalities in processing speed in pwMS. Methods: A sample of 50 pwMS and 32 matched healthy control (HC) subjects was tested with the auto-SDMT and the Brief International Cognitive Assessment for MS (BICAMS). Results: The percentages of MS participants impaired on the auto-SDMT and the traditional oral SDMT were 34% and 32%, respectively. Excellent convergent validity was found between the two tests (MS: r = −0.806, p < 0.001 and HC: r = −0.629, p < 0.001). The auto-SDMT had a similar sensitivity and specificity to the traditional oral SDMT in predicting overall impairment on the BICAMS. Conclusion: The auto-SDMT is a sensitive measure for detecting processing speed deficits in pwMS. The test, the first entirely computer administrated oral response version of the SDMT, uses speech recognition technology, thereby eliminating the need for a human tester. Replication of the results is required in a larger representative sample of pwMS.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".