Historic Stroke Motor Severity Score Predicts Progression in TIA/Minor Stroke
Bibliographic record
Abstract
BACKGROUND: transient ischemic attack (tIA) and minor stroke have a high risk of early neurological deterioration, and patients who experience early improvement are at risk of deterioration. We generated a score for quantifying the worst reported motor and speech deficits and assessed whether this predicted outcome. METHODS: 510 tIA or minor stroke (NIHSS>4) patients were included. the Historical Stroke Severity Score (HSSS) prospectively quantified the patient's description of the worst motor or speech deficits. the HSSS was rated at the time of first assessment with more severe deficits scoring higher. Motor HSSS included assessments of arm and leg motor power (score total 0-5). Speech HSSS assessed severity of dysarthria and aphasia (total 0-3). the association between motor and speech HSSS and symptom progression was assessed during the 90-day follow-up period. RESULTS: the proportion of patients in each category of the motor HSSS was 0: 43% (216/510), 1: 22%(110/510), 2: 17% (89/510), 3: 7% (37/510), 4: 5% (28/510) and 5: 6% (30/510). Motor HSSS was associated with symptom progression (p=0.004) but not recurrent stroke. Speech HSSS was not associated with either progression or recurrent stroke. Motor HSSS predicted disability (p=0.002) and intracranial occlusion (p=0.012). Disability increased with increasing motor HSSS. CONCLUSIONS: taking a detailed history about the severity of motor deficits, but not speech, predicted outcome in tIA and minor stroke patients. A score based on the patient's description of the severity of motor symptoms predicted symptom progression, intracranial occlusion and functional outcome, but not recurrent stroke in a tIA and minor stroke population. Le Historical Stroke Severity Score moteur prédit la progression d'un accès ischémique cérébral transitoire / d'un accident vasculaire cérébral mineur.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".