Processing speed test: Validation of a self-administered, iPad <sup>®</sup> -based tool for screening cognitive dysfunction in a clinic setting
Bibliographic record
Abstract
Background: Cognitive dysfunction is common in multiple sclerosis (MS) patients and has important consequences for daily activities, yet, unlike motor function, is not routinely assessed in the clinic setting. We developed the Processing Speed Test (PST), a self-administered iPad ® -based tool to measure MS-related deficits in processing speed. Objective: To determine whether the PST is valid for screening cognitive dysfunction by comparing it to the paper-and-pencil Symbol Digit Modalities Test (SDMT). Methods: We assessed PST test–retest reliability, sensitivity of PST and SDMT in discriminating MS patients from healthy controls (HC), convergent validity between PST and SDMT, correlations between T2 lesion load and PST and SDMT, and PST performance with and without technician present during administration. Results: PST had excellent test–retest reliability, was highly correlated with SDMT, was slightly more sensitive than SDMT in discriminating MS from HC groups, and correlated better with cerebral T2 lesion load than did SDMT. Finally, PST performance was no different with or without a technician in the testing environment. Conclusion: PST has advantages over SDMT because of its efficient administration, scoring, and potential for medical record or research database integration. PST is a practical tool for routine screening of processing speed deficits in the MS clinic.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".