Prevalence of Poststroke Neurocognitive Disorders Using National Institute of Neurological Disorders and Stroke-Canadian Stroke Network, VASCOG Criteria (Vascular Behavioral and Cognitive Disorders), and Optimized Criteria of Cognitive Deficit
Bibliographic record
Abstract
Background and Purpose—The prevalence of poststroke neurocognitive disorder (NCD) has yet to be accurately determined. The primary objective of the present study was to optimize operationalization of the criterion for NCD by using an external validity criterion. Methods—The GRECOG-VASC cohort (Groupe de Reflexion pour l'Evaluation Cognitive Vasculaire) of 404 stroke patients with cerebral infarct (91.3%) or hemorrhage (18.7%) was assessed 6 months poststroke and 1003 healthy controls, with the National Institute of Neurological Disorders and Stroke-Canadian Stroke Network standardized battery. Three dimensions of the criterion for cognitive impairment were systematically examined by using the false-positive rate as an external validity criterion. Diagnosis of mild and major NCD was based on the VASCOG criteria (Vascular Behavioral and Cognitive Disorders). The mechanisms of functional decline were systematically assessed. Results—The optimal criterion for cognitive impairment was the shortened summary sco...
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".