P.044 The impact of working hours on rapid GFAP measurement in acute stroke: evaluation of sampling bias in an ongoing prospective study
Bibliographic record
Abstract
Background: Glial fibrillary acidic protein (GFAP), a brain-specific biomarker, shows promise in differentiating intracerebral hemorrhage (ICH) from acute ischemic stroke (IS) and stroke mimics (SM). A novel point-of-care platform measures GFAP in minutes, yet requires centrifugation to obtain plasma. We aim to determine whether participants recruited in an ongoing prospective biomarker study (during working hours) differ from non-recruited patients. Methods: An exploratory analysis of undifferentiated stroke <24h from onset, where plasma GFAP levels (pg/ml) are measured (i-STAT Alinity) at hospital arrival. Clinical characteristics are compared among recruited and non-recruited patients. Results: Among the first 101 patients recruited, mean (±SD) age (70.8±14.5 years), % females (48%), and median (IQR) NIHSS (9(3-20) were similar to the 270 non-recruited patients (70.3±16.3 years, 51% females, NIHSS 7 (3-17), respectively) in the same time period. Median ASPECTS was slightly lower in recruited patients (10(9-10) vs (10(10-10)) (p=0.03). ICH and SM were more common among non-recruited (52% IS/13% ICH/32% SM) compared to recruited patients (67% IS/5% ICH/29% SM, p=0.002), while large-vessel occlusion was more common among those recruited (44% vs 19%, p=0.001). Conclusions: Clinical characteristics do not differ among recruited vs. non-recruited patients in an ongoing biomarker study, yet sampling bias exists regarding underlying stroke condition, with efforts to mitigate this going forward.
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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 | 0.082 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".