Computerized Neuropsychological Test Battery CogniSoft for Assessment of Cognition in Patients with Multiple Sclerosis
Bibliographic record
Abstract
Cognitive dysfunction is a leading cause of disability in multiple sclerosis (MS) and is associated with unemployment, need of assistance with daily activities and poor quality of life. The introduction of neuropsychological testing and monitoring of cognitive status as part of the overall evaluation of MS patients in parallel with clinical and paraclinical parameters is highly recommended. Recent studies have demonstrated a better perception and preference for computerized cognitive tests than classic variants, with no significant difference in results. In accordance with global trends, a bilingual computer system CogniSoft for assessment and rehabilitation of cognitive status in persons with MS has been developed, including: 1) a set of diagnostic tests for evaluation of memory and executive functions based on the nature of Brief International Cognitive Assessment for MS (BICAMS); 2) a set of games for cognitive rehabilitation. Questionnaire for depression (Beck Depression Inventory – BDI-II) will be filled before conduction of the neuropsychological tests for differentiation of possible depression which could interfere with the results. The CogniSoft information system will incorporate two approaches for evaluation of neuropsychological results which will allow early detection of cognitive impairments in these patients, which will initiate timely cognitive rehabilitation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".