Impact of Periprocedural Hemoglobin Level on the Outcomes of Endovascular Thrombectomy in Acute Ischemic Stroke Patients
Bibliographic record
Abstract
ABSTRACT Background: Endovascular thrombectomy (EVT) is the gold standard treatment for acute ischemic stroke (AIS) patients with large vessel occlusion (LVO). Multiple factors can influence EVT outcomes, including procedural and patient-related variables. This meta-analysis investigated the impact of periprocedural hemoglobin (Hb) levels on EVT outcomes. Methods: We performed a comprehensive literature search across PubMed, Scopus, Web of Science and Cochrane CENTRAL. We analyzed the mean difference (MD) in Hb levels between good (modified Rankin Scale [mRS] 0–2) and poor (mRS 3–6) prognosis groups. We calculated pooled odds ratios (OR) for Hb levels as a predictor of prognosis and compared mortality and symptomatic intracranial hemorrhage (sICH) across different Hb levels. Results: The analysis included 921 patients from four studies. Patients in the good prognosis group had significantly higher Hb levels (MD: 0.48 g/dL, 95% CI: [0.2, 0.75], P = 0.0007). Each 1 g/dL increase in Hb was associated with a 22% increase in the odds of achieving a good three-month prognosis (OR: 1.22, 95% CI: [1.13, 1.33], P < 0.00001). Patients with Hb levels ≤13 g/dL in males and ≤12 g/dL in females were 1.69 times more likely to experience mortality (OR: 1.69, 95% CI: [1.1, 2.59], P = 0.02). No significant difference was observed in sICH occurrence between anemic and non-anemic patients. Conclusion: Higher Hb levels may be associated with improved prognosis, and lower Hb levels might increase mortality risk in AIS-LVO patients undergoing EVT. Further research is needed to validate these findings.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.018 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".