Emotional stroke: clinicoradiologic profile.
Bibliographic record
Abstract
OBJECTIVE: Relationships of emotional stressors (ES), stroke subtypes, and mechanisms. METHODS: Stroke risk factors (RF) (n = 26), personal/emotional stressors (ES), and life change units (Holmes and Rahe scale) were evaluated. RESULTS: Of patients with cerebral infarct (n = 1000), 36 (3.6%) had significant ES. Infarct topography involving partial anterior circulation (PAC) (n = 18; 50%) was most frequent. Etiologies (TOAST classification) of small vessel (n = 11; 31%), "other" (coagulopathy, vasculopathy) (n = 8; 22%), and unknown (n = 5; 14%) (P = 0.05). Cognitive impairment was seen most frequently in the ES group (28/36; 78% versus 606/1000; 61%; P = 0.03). Neurologic deficit by Canadian Neurologic Scale (mean 10.3, range 11.5-4.5) was mild. Disability (Rankin scale) was minimal: mild grade 0-2 (n = 30/36; 83%), moderate grade 3-4 (n = 4/36; 11%) (P = 0.0001). MRI showed that subcortical lesion topography (n = 23/36; 64%) predominated over cortical lesions (n = 13/36; 36% P = 0.05). CONCLUSIONS: With ES and stroke, partial anterior circulation stroke and subcortical lesions are significant, unknown and other causes are most frequent, deficit and handicap are mild, but neurocognitive deficit is frequent.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.007 | 0.001 |
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".