Glial fibrillary acidic protein for the early diagnosis of intracerebral hemorrhage: Systematic review and meta-analysis of diagnostic test accuracy
Bibliographic record
Abstract
Background and aims Glial fibrillary acidic protein (GFAP) has shown promise in several studies for its ability to diagnose intracerebral hemorrhage (ICH). We evaluated the diagnostic accuracy of blood GFAP level to differentiate (ICH) from acute ischemic stroke (AIS) and stroke mimics, both overall, and in the first three hours after symptom onset. Methods We searched multiple databases, without language restriction, from inception until December 2017. Hierarchical summary receiver operating characteristic (HSROC) modeling was used to meta-analyze results. We conducted subgroup analyses restricted to blood samples collected within 0–60, 60–120, and 120–180 min time groups after symptom onset, to evaluate diagnostic accuracy in the early pre-hospital phase. Between and within study heterogeneity was explored using meta-regression. Results The search identified 199 potentially relevant citations from which 11 studies involving 1297 participants (350 ICH, 947 AIS, or mimic) were included. The pooled sensitivity, specificity, and area under the HSROC curve were 0.756 (95% CI 0.630–0.849), 0.945 (95% CI 0.858–0.980), and 0.904 (95% CI 0.878–0.931), respectively. Differences in assays used, but not the other covariates, partially explained between-study heterogeneity ( p = 0.034). The summary estimates for the 0–60, 60–120, and 120–180 min subgroups were comparable to the primary analysis and there was no statistically significant difference in diagnostic accuracy between subgroups. Conclusions GFAP is a promising diagnostic biomarker for ICH diagnosis in the early pre-hospital phase. Test accuracy is affected by assay subtype, but there are still unexplained sources of heterogeneity. High quality, international multi-center trials are warranted to develop and validate a point-of-care GFAP assay for the rapid triage and evaluation of acute stroke in the pre-hospital setting.
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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 | 0.021 | 0.064 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".