B.7 Rapid Glial Fibrillary Acidic Protein (GFAP) analysis in acute stroke: feasibility and diagnostic potential
Bibliographic record
Abstract
Background: Brain-specific glial fibrillary acidic protein (GFAP) can discriminate stroke type [ischemic stroke (AIS), intracerebral hemorrhage (ICH), stroke mimics (SM)]. Novel point-of-care technology (GFAP levels <15 minutes) is a promising diagnostic tool. We aim to evaluate the feasibility of rapid GFAP analysis in acute stroke. Methods: Exploratory analysis of an ongoing prospective study of suspected undifferentiated stroke <24h from onset. Rapid plasma GFAP levels (pg/mL) are measured at hospital arrival using the i-STAT Alinity® instrument and commercially-available cartridges. Study endpoints include quantitative GFAP levels according to final diagnosis and time from stroke onset. Results: Among 200 patients (mean(±SD) 70.7±15.5 years, 44.5% female, median (IQR) NIHSS 9(4-19), diagnosis was AIS (n=132 (59 large-vessel occlusion), ICH (n=17), and SM (n=51). Median time from hospital arrival to GFAP result was 56.0 (47.0-69.5) minutes. Median rapid GFAP levels were highest in ICH (878.0 (70.5-3,906.5) pg/mL) compared to AIS (49.5 (29.0-95)pg/mL) and SM (29(29-64)pg/mL), p=0.001. Median GFAP was higher in AIS-known onset >4.5h (n=9) (110.0 (44.0-216.0) pg/mL) compared to AIS<4.5h (40.5 (29.0-68.8) pg/mL) (n=72), (p=0.047), while AIS-unknown onset (n=51) (68.0 (29.0-108.5) pg/mL) fell between these two groups, likely reflecting the subgroup’s heterogeneity. Conclusions: Preliminary findings suggest that rapid GFAP analysis is feasible in acute stroke and may inform treatment decisions.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".