Time-of-Day Could Affect Cognitive Screening Performance in Older Patients with TIA and Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The impact of time-of-day on the cognitive performance of older patients with limited cognitive reserve after a transient ischemic attack (TIA) or stroke, and on short cognitive tests, such as the Montreal Cognitive Assessment (MoCA), is unknown. We retrospectively studied whether morning versus afternoon assessment might affect the classification of patients aged 70 or older as severe (SCI), mild (MCI), and no (NCI) cognitive impairment by the MoCA. METHODS: Morning (12 p.m. or earlier) versus afternoon (later than 12 p.m.) proportions of SCI (MoCA score <20), MCI (MoCA score 25-20) and NCI (MoCA score ≥26) were compared in a cohort of patients aged ≥70, attending a rapid-access TIA/stroke clinic. RESULTS: Of 278 patients, 113 (40.6%) were tested in the morning and 165 (59.4%) in the afternoon. The proportion with SCI was greater in the afternoon than in the morning (10.9 vs. 1.8%, respectively, p = 0.004), with no difference in age, education, diagnosis, disability, or vascular risk factors. CONCLUSIONS: Time-of-day appears to affect cognitive performance of older patients after they undergo TIA and minor stroke. If our cross-sectional findings are confirmed in cross-over studies with repeated testing, timing of assessments should be considered in clinical practice and in research studies.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".