Comprehensive cognitive neurological assessment in stroke
Bibliographic record
Abstract
BACKGROUND: Cognitive syndromes (CS) after stroke may be important to measure and monitor for management and emerging therapies. AIM: To incorporate known behavioral neurological and neuropsychiatric syndromes into a bedside cognitive assessment in patients with stroke. METHODS: A validated cognitive examination (comprehensive cognitive neurological test in stroke, Coconuts) was administered during the first month of stroke presentation and analyzed according to five large-scale networks for cognition and correlated with neuropsychological tests. Validity testing of the test was performed for overall sensitivity, specificity, positive predictive value and negative predictive value to stroke in comparison with MRI diagnosis of stroke as well as discriminant validity, construct validity and inter-rater reliability. RESULTS: Overall the sensitivity of the Coconuts scale was 91% and specificity 35%, PPV 88% and NPV 41% vs stroke lesions using MRI. Cognitive syndrome frequencies: frontal network syndrome frequency was 908/1796 (51%), left hemisphere network syndrome frequency was 646/1796 (36%), right hemisphere network included 275/1796 (15.3%), occipitotemporal network for complex visual processing 107/1796 (6%), the hippocampal limbic network for amnesias and emotional disorders 397/1796 (22%) and miscellaneous network syndromes 481/1796 (27%). CONCLUSION: The Coconuts is a valid and practical test of a comprehensive array of known behavioral neurological and neuropsychiatric syndromes in patients with stroke.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".