Laboratory biomarkers of delayed cerebral ischemia following subarachnoid hemorrhage: A systematic review
Bibliographic record
Abstract
Delayed cerebral ischemia (DCI) substantially contributes to disability and death in subarachnoid hemorrhage (SAH) patients; however, its pathophysiology is incompletely understood and diagnostic and therapeutic strategies are lacking. Biomarkers may help to elucidate the pathophysiology, optimize early diagnosis, or provide treatment targets. We systematically searched PubMed and Embase on October 13, 2021, for studies that evaluated at least one laboratory biomarker in patients with DCI, using the most up-to-date definition of DCI as proposed by a panel of experts in 2010. Quality of studies was assessed using the Newcastle-Ottawa Scale or Cochrane Collaboration's risk of bias assessment tool. Biomarkers of clinical and radiological DCI were analyzed separately. Results were meta-analyzed if possible, otherwise narratively reviewed. Biomarkers were classified as significant, inconclusive, or nonsignificant. We defined validated biomarkers as those with significant results in meta-analyses, or in at least two studies using similar methodologies within the same time interval after SAH. The search yielded 209 articles with 724 different biomarkers; 166 studies evaluated 646 biomarkers of clinical DCI, of which 141 were significant and 7 were validated biomarkers (haptoglobulin 2-1 and 2-2, ADAMTS13, vWF, NLR, P-selectin, F2-isoprostane); 78 studies evaluated 165 biomarkers of radiological DCI, of which 63 were significant and 1 was a validated biomarker (LPR). Hence, this review provides a selection of seven biomarkers of clinical DCI and one biomarker of radiological DCI as most promising biomarkers of DCI. Future research should focus on determining the exact predictive, diagnostic, and therapeutic potentials of these biomarkers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".