NINDS AIREN neuroimaging criteria do not distinguish stroke patients with and without dementia
Bibliographic record
Abstract
OBJECTIVE: To determine the utility of the neuroimaging component within the National Institute of Neurological Disorders and Stroke (NINDS) Association Internationale pour la Recherche et l'Enseignement en Neurosciences (AIREN) criteria for vascular dementia for distinguishing between patients with and without dementia in the context of cerebrovascular disease. METHOD: One hundred twenty-five poststroke patients age > or =75 (27 with and 98 without poststroke dementia) from representative hospital-based stroke registers in the North East of England were evaluated using a 1.5 T MR scanner. The proportion of patients with and without poststroke dementia meeting the imaging component of the NINDS AIREN criteria was determined, and hippocampal atrophy (measured using the Schelten scale) was compared between the two groups. RESULTS: There were no significant differences between the patients with and without poststroke dementia on any criteria of the imaging parameters within the NINDS AIREN criteria. In addition, there were no significant differences in the number or size of cortical or subcortical infarcts between the two groups, with 13 patients without dementia having cortical infarcts >50 mm. Patients with dementia had greater hippocampal atrophy (right: Mann-Whitney U test, Z = 2.5, p = 0.01; left: Mann-Whitney U test, Z = 2.5, p = 0.01). CONCLUSION: The neuroimaging component of the NINDS AIREN criteria does not distinguish between older patients with and without poststroke dementia.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".