Clinical Diagnosis of Lacunar Stroke in the First 6 Hours After Symptom Onset
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Although the pathophysiological heterogeneity of stroke may be highly relevant to the development of acute-phase therapies, discriminating between ischemic stroke subtypes soon after onset remains a challenge. We conducted a study of the accuracy of a clinical diagnosis of lacunar stroke in the first 6 hours after symptom onset. METHODS: We analyzed data from 1367 patients in the Glycine Antagonist In Neuroprotection (GAIN) Americas trial. The Trial of ORG10172 in Acute Stroke Treatment (TOAST) category "small vessel (lacunar)" disease at day 7 or at hospital discharge was used as the reference standard to determine the accuracy of a diagnosis of a lacunar stroke made within 6 hours of symptom onset using the Oxfordshire Community Stroke Project (OCSP) classification "LACS." Outcome was analyzed by comparing the proportions of patients classified as "LACS" at baseline or "small vessel (lacunar)" at 7 days who were dead or dependent at 3 months. RESULTS: The positive predictive value of an OCSP diagnosis of a lacunar stroke was 76% (95% CI: 69% to 81%; sensitivity 64% [95% CI: 58% to 70%]; specificity 96% [95% CI: 95% to 97%]; negative predictive value 93% [95% CI: 92% to 94%]; accuracy 91% [95% CI: 89% to 92%]). The 3-month outcomes of patients classified as either OCSP "LACS" within 6 hours of onset or TOAST "small vessel (lacunar)" at 7 days were not significantly different. CONCLUSIONS: An OCSP LACS diagnosis made within 6 hours of stroke onset is reasonably predictive of a final diagnosis of "small vessel (lacunar)" disease made using TOAST criteria and has a similar relationship to outcome at 3 months.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".