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Record W4406153289 · doi:10.14740/jnr860

The Role of Erythrocyte Sedimentation Rate as a Prognostic Factor in Intracerebral Hemorrhage

2025· article· en· W4406153289 on OpenAlexvenueno aff
Ereida Rraklli, Frenki Gjika, Oneda Cibuku

Bibliographic record

VenueJournal of Neurology Research · 2025
Typearticle
Languageen
FieldMedicine
TopicBlood properties and coagulation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineIntracerebral hemorrhageErythrocyte sedimentation rateSedimentationInternal medicineGastroenterologySubarachnoid hemorrhageGeology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.363
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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