The Montreal cognitive assessment is superior to national institute of neurological disease and stroke-Canadian stroke network 5-minute protocol in predicting vascular cognitive impairment at 1 year
Bibliographic record
Abstract
BACKGROUND: The predictive ability of National Institute of Neurological Disease and Stroke-Canadian Stroke Network (NINDS-CSN) 5-minute protocol and Montreal Cognitive Assessment (MoCA) administered sub-acutely and at the convalescent phase after stroke for significant vascular cognitive impairment (VCI) at 1 year is unknown. We compared prognostic values of these tests. METHODS: Patients with ischemic stroke and transient ischemic attack (TIA) received MoCA sub-acutely (within 2 weeks) and 3-6 months after stroke followed by a formal neuropsychological evaluation at 1 year. The total score of NINDS-CSN 5-minutes protocol was derived from MoCA. Moderate-severe VCI was defined as neuropsychological impairment in ≥ 3 domains. Area under the receiver operating characteristic curve (AUC) analyses were conducted to establish the optimal cutoff points and discriminatory properties of the MoCA and NINDS-CSN 5-minute protocol in detecting moderate-severe VCI. RESULTS: Four hundre patients were recruited at baseline. Of these, 291 received a formal neuropsychological assessment 1 year after stroke. 19% patients had moderate-severe VCI. The MoCA was superior to the NINDS-CSN 5-minute protocol [sub-acute AUCs: 0.89 vs 0.80, p < 0.01; 3-6 months AUCs: 0.90 vs 0.83, p < 0.01] in predicting for moderate-severe VCI at 1 year. At respective cutoff points, MoCA had significantly higher sensitivity than the NINDS-CSN 5-minute protocol at baseline (p = 0.01) and 3-6 months (p = 0.04). CONCLUSIONS: MoCA administered sub-acutely and 3-6 months after stroke is superior to the NINDS-CSN 5-minute protocol in predicting moderate-severe VCI at 1 year.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".