The Role of Erythrocyte Sedimentation Rate as a Prognostic Factor in Intracerebral Hemorrhage
Bibliographic record
Abstract
Background: Intracerebral hemorrhage (ICH) is a severe type of stroke with high mortality and long-term disability rates. Early identification of patients at high risk of poor outcomes is crucial. Erythrocyte sedimentation rate (ESR), a nonspecific marker of inflammation, may serve as a prognostic indicator in ICH patients. Methods: This retrospective cohort study analyzed 82 ICH patients from December 2021 to February 2024. Patients were divided into high ESR (≥ 20 mm/h) and normal ESR (< 20 mm/h) groups. Demographic, clinical, and laboratory data were collected, and outcomes were assessed using the modified Rankin Scale (mRS) at 6 months post-ICH. Logistic regression analyzed the association between ESR levels and outcomes. Results: Elevated ESR was observed in 35 patients (42.7%). Mortality rate at 6 months was significantly higher in the elevated ESR group (20% vs. 8.5%, P = 0.03), with elevated ESR being an independent predictor of mortality (odds ratio (OR) = 2.5). Functional impairment (mRS > 3) was also higher in the elevated ESR group (65.7% vs. 34%, P = 0.002), with elevated ESR independently associated with functional impairment (OR = 3.1). Conclusions: Elevated ESR at admission is an independent predictor of mortality and functional impairment in ICH patients. ESR can aid in risk stratification and guide clinical decision-making, emphasizing the role of inflammation in ICH.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".