Cognitive screening in the acute stroke setting
Bibliographic record
Abstract
BACKGROUND: current literature suggests that two-thirds of patients will have cognitive impairment at 3 months post-stroke. Post-stroke cognitive impairment is associated with impaired function and increased mortality. UK guidelines recommend all patients with stroke have a cognitive assessment within 6 weeks. There is no 'gold standard' cognitive screening tool. The Montreal cognitive assessment (MoCA) is more sensitive than the Mini-Mental State Examination (MMSE) in mild cognitive impairment and for cognitive impairment in the non-acute post-stroke setting and in a Chinese-speaking acute stroke setting. METHODS: a convenience sample of 50 patients, admitted with stroke or transient ischaemic attack (TIA), were screened within 14 days, using the MoCA and the MMSE. RESULTS: the mean MoCA was 21.80 versus a mean MMSE of 26.98; 70% were impaired on the MoCA (cut-off <26) versus 26% on MMSE (cut-off <27). The MoCA could be completed in <10 min in 90% of cases. CONCLUSION: the MoCA is easy and quick to use in the acute stroke setting. Further work is required to determine whether a low score on the MoCA in the acute stroke setting will predict the cognitive and functional status and to explore what the best cut-off should be in an acute post-stroke setting.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".