Phototest for neurocognitive screening in multiple sclerosis
Bibliographic record
Abstract
Multiple Sclerosis (MS) is one of the most common neurological disorders. Cognitive dysfunction is considered a clinical marker of MS, where approximately half of patients with MS have cognitive impairment. OBJECTIVE: The Phototest (PT) is a brief cognitive test with high diagnostic sensitivity, accuracy and cost-effectiveness for detecting cognitive deterioration. Our aim was to test the utility of the PT as a neurocognitive screening instrument for MS. METHODS: The study enrolled 30 patients with different types of MS from an outpatient clinic as well as 19 healthy participants. In conjunction with the PT, the Montreal Cognitive Assessment (MoCA), Barthel Index (BI), Expanded Disability Status Scale (EDSS), and Fatigue Severity Scale (FSS) were administered. RESULTS: The MS group obtained significantly lower results on all domains of the PT, except for the naming task. The PT showed good concurrent validity with the MoCA. In direct comparison to the MoCA, PT showed a greater area under the curve and higher levels of sensitivity and specificity for MS neurocognitive impairments. A cut-off score of 31 on the Phototest was associated with sensitivity of 100% and specificity of 76.7%. CONCLUSION: The PT is a valid, specific, sensitive and brief test that is not dependent on motor functions. The instrument could be an option for neurocognitive screening in MS, especially in identifying cases for further neuropsychological assessment and intervention.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.003 | 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".