Who should undergo a comprehensive cognitive assessment after a stroke?
Bibliographic record
Abstract
Objective To validate the ability of a specifically developed cognitive risk score to identify patients at risk of poststroke neurocognitive disorders (NCDs) who are eligible for a comprehensive cognitive assessment. Methods After assessing 404 patients (infarct 91.3%) in the Groupe de Réflexion pour l9Evaluation Cognitive VASCulaire (GRECogVASC) cross-sectional study with the National Institute of Neurological Disorders and Stroke–Canadian Stroke Network battery 6 months after stroke, we used multivariable logistic regression and bootstrap analyses to determine factors associated with NCDs. Independent, internally validated factors were included in a cognitive risk score. Results Cognitive impairment was present in 170 of the 320 patients with a Rankin Scale score ≥1. The backward logistic regression selected 4 factors (≥73% of the permutations): NIH Stroke Scale score on admission ≥7 (odds ratio [OR] 2.73, 95% confidence interval [CI] 1.29–4.3, p = 0.005), multiple strokes (OR 3.78, 95% CI 1.6–8, p = 0.002), adjusted Mini-Mental State Examination (MMSEadj) score ≤27 (OR 6.69, 95% CI 3.9–11.6, p = 0.0001), and Fazekas score ≥2 (OR 2.34, 95% CI 1.3–4.2, p = 0.004). The cognitive risk score computed with these 4 factors provided good calibration, discrimination (overoptimism-corrected C = 0.793), and goodness of fit (Hosmer-Lemeshow test p = 0.99). A combination of Rankin Scale score ≥1, cognitive risk score ≥1, and MMSEadj score ≥21 selected 230 (56.9%) of the 404 patients for a comprehensive assessment. This procedure yielded good sensitivity (96.5%) and moderate specificity (43%; positive predictive value 0.66, negative predictive value 0.91) and was more accurate (p ≤ 0.03 for all) than the sole use of screening tests (MMSE or Montréal Cognitive Assessment). Conclusion The GRECogVASC cognitive risk score comprises 4 easily documented factors; this procedure helps to identify patients at risk of poststroke NCDs who must therefore undergo a comprehensive assessment. ClinicalTrials.gov identifier: NCT01339195.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".