High Specific C-Reactive Protein in Prediction of the Early Outcome of Primary Intra Cerebral Hemorrhage
Bibliographic record
Abstract
Background: Primary intra cerebral hemorrhage (ICH) has high rate of disability and death. Many factors was supposed to be predictors of the outcome. The significance of measuring C-reactive protein (CRP) levels to predict the outcome is uncertain, and data have been controversial. The objective of our clinical study was to determine the relationship of hs-CRP levels with bad outcome. The authors tested if (independent of confounding factors) hs-CRP levels was elevated on admission (< 24 hours after ictus). Fifty patients with acute spontaneous hemorrhagic stroke, within 24 hours of onset confirmed by CT brain were admitted at neurology department, Mansoura University Hospital from June 2017 to September 2018. Age and sex cross-matched 50 healthy persons were studied as control group. Patient and control groups were subjected to full history, general and neurological examination, GCS on admission, National Institute of Health Stroke Scale (NIHSS) on admission, ICH score and Canadian scale on admission and after 30 days, Venous Blood samples were taken within 24 hours of onset and tested for routine laboratory investigations (liver function, serum creatinine, CBC and blood glucose) and High Sensitive C-Reactive Protein level using Enzyme Immunoassay Test Kit. Computed tomography (CT) brain was repeated 72 hours later. Multivariable regression analyses were used to evaluate associations of hs-CRP concentration and ICH outcome. Kaplan–Meier analysis was used for survival. Results: This study revealed that Hs-CRP is significantly higher in patient group (9.3 mg/l) when compared to control group (0.68 mg/dl) with p value < 0,001. There was statistically significant correlation between NIHSS and hs-CRP levels but there was no statistically significant correlation between hs-CRP levels and stroke outcome. Conclusion: Taking these covariates into multivariable analysis revealed that there is correlation between hs-CRP and hemorrhagic stroke but it cannot be used as a predictor of its outcome.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".