Comprehensive cognitive neurological assessment in stroke
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 it