Tests of Information Processing Speed
Bibliographic record
Abstract
Reduction in information processing speed (IPS) is a key deficit in multiple sclerosis (MS). The Paced Auditory Serial Addition Test (PASAT), Symbol Digit Modalities Test (SDMT), and Computerized Test of Information Processing (CTIP) are used to measure IPS. Both the PASAT and SDMT are sensitive to deficits in IPS. The CTIP, a newer task, also shows promise. The PASAT has several limitations, and it is often perceived negatively by patients. Yet little supporting quantitative evidence of such perceptions has been presented. Therefore, in this study, subjective ratings of likeability, difficulty, and appropriateness of the PASAT, CTIP, and SDMT were obtained. Ratings were compared between MS patients and healthy controls. It was hypothesized that ratings of the PASAT would differ significantly from those of the SDMT and CTIP. The relationship between subjective ratings and objective performance was evaluated. Sixty-nine MS patients and 68 matched controls rated the three tests in terms of likeability, difficulty, and appropriateness for capturing cognitive deficits often associated with MS using a Likert scale. Both groups rated the PASAT as most difficult and least likeable. The MS group rated the PASAT and SDMT as more appropriate for measuring MS-related deficits than the CTIP. Subjects who performed better on the PASAT were more likely to rate it as easier. Ratings of the SDMT and CTIP did not vary consistently with performance. The findings lend quantitative support to the common belief that the PASAT is perceived as unpleasant. Other tests are available that are similarly sensitive to deficits in IPS and more palatable to the patient.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".