161 Qualitative aspects of cognition in MS & audit of MoCA in LTHTR natalizumab cohort
Bibliographic record
Abstract
Cognitive impairment in MS is very relevant to treatment decisions & management. It may be a presenting clinical feature of natalizumab-associated PML ; conversely improvement in cognition may be seen in treatment of MS. There is some evidence for the Montreal Cognitive Assessment as a screening tool in MS. MoCA was performed as a cognitive baseline for PML surveillance for patients on natalizumab. 83 natalizumab-treated patients were identified. Moca scores were available for 72 patients. Scores ranged from 15 -30 (Score is out of 30 and the cut off for ‘normal’ is >/=26). Average score 25.7. Age range 18–69, average age 47. 30 patients (41.7%) scored below cut off. Subsection scores were available for 27 patients. These will be described in more detail. Patients with low scores were often observed to have frequent DNA letters on file. Two patients had longitudinal data. This audit suggests a high prevalence of cognitive impairment in line with reports which in some cases seems quite severe. MoCA seems to be a usefool screening tool and alerts us to the need for detailed consent & follow-up as well as indicating qualitative aspects of cognitive dysfunction to enable practical strategies to be employed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".