[P3–302]: DETECTION OF COGNITIVE IMPAIRMENT AFTER ACUTE ISCHEMIC STROKE: VALIDATION OF BRIEF AND MORE COMPREHENSIVE COGNITIVE SCREENING INSTRUMENTS AND IMPLICATIONS FOR BEST PRACTICE
Bibliographic record
Abstract
Cognitive impairments after cerebrovascular accident (CVA) are associated with greater disability, mortality, and rising dementia rates over time. When evaluating screening approaches, including Hachinski's 2006 NINDS-CSN harmonization standards (1) and a MoCA 5-Minute protocol (M5M) (2), we posit that best cognitive screening practice will be (a) feasible in busy clinical settings, (b) sensitive and specific for deficits predicting practical outcomes, and (c) valid despite CVA-related limitations (e.g., aphasias, visuomotor deficits). Our group has been developing such an approach. 222 patients admitted for CVA were administered all or part of the LASS-I (Lexington Acute Stroke Scale – Inpatient), a 20-minute cognitive screen and received Barthel Index (BI) and Modified Rankin Scale (MRS) ratings. M5M scores were extracted from the LASS-I. M5M scores predicted both BI (R2 = 0.16, F(1, 153) = 28.63, p < .001) and MRS (R2 = 0.16, F(1, 161) = 30.32, p < .001). LASS-I score added predictive value for the BI (R2 = 0.22, F(2, 152) = 21.37, p < .001) and some predictive value for the MRS (R2 = .18, F(2, 160) = 17.06, p < .001). After analyzing individual domain-based contributions, combining best domain-based performances from the M5M (Executive Functions/Language) and LASS-I (Visuospatial/Construction & Praxis) was comparable to administering either instrument in full when predicting BI (R2 = .20, F (2, 144) = 18.35, p < .001) and MRS (R2 = .18, F (2, 152) = 16.62, p < .001). Effect sizes for all regression models were medium by Cohen's f2. ROC curve analysis yielded areas under the curve for the M5M at 0.74 (MRS) and 0.73 (BI), for the LASS-I at 0.79 (MRS and BI), and for the combined approach at 0.77 (MRS) and 0.72 (BI).
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".