Ultra-Early Differential Diagnosis of Acute Cerebral Ischemia and Hemorrhagic Stroke by Measuring the Prehospital Release Rate of GFAP
Bibliographic record
Abstract
BACKGROUND: Plasma glial fibrillary acidic protein (GFAP) and tau are promising markers for differentiating acute cerebral ischemia (ACI) and hemorrhagic stroke (HS), but their prehospital dynamics and usefulness are unknown. METHODS: We performed ultra-sensitivite single-molecule array (Simoa®) measurements of plasma GFAP and total tau in a stroke code patient cohort with cardinal stroke symptoms [National Institutes of Health Stroke Scale (NIHSS) ≥3]. Sequential sampling included 2 ultra-early samples, and a follow-up sample on the next morning. RESULTS: We included 272 cases (203 ACI, 60 HS, and 9 stroke mimics). Median (IQR) last-known-well to sampling time was 53 (35-90) minutes for initial prehospital samples, 90 (67-130) minutes for secondary acute samples, and 21 (16-24) hours for next morning samples. Plasma GFAP was significantly higher in patients with HS than ACI (P < 0.001 for <1 hour and <3 hour prehospital samples, and <3 hour secondary samples), while total tau showed no intergroup difference. The prehospital GFAP release rate (pg/mL/minute) occurring between the 2 very early samples was significantly higher in patients with HS than ACI [2.4 (0.6-14.1)] versus 0.3 (-0.3-0.9) pg/mL/minute, P < 0.001. For cases with <3 hour prehospital sampling (ACI n = 178, HS n = 59), a combined rule (prehospital GFAP >410 pg/mL, or prehospital GFAP 90-410 pg/mL together with GFAP release >0.6 pg/mL/minute) enabled ruling out HS with high certainty (NPV 98.4%) in 68% of patients with ACI (sensitivity for HS 96.6%, specificity 68%, PPV 50%). CONCLUSIONS: In comparison to single-point measurement, monitoring the prehospital GFAP release rate improves ultra-early differentiation of stroke subtypes. With serial measurement GFAP has potential to improve future prehospital stroke diagnostics.
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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) | 0.000 | 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) | 0.001 | 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".