Red cell distribution width (RDW) and Alberta stroke programme early computed tomography score (ASPECTS) are correlated in predicting mortality in geriatric ischemic stroke patients
Bibliographic record
Abstract
Objective: Ischemic stroke is one of the leading causes of disability and death in elderly people worldwide. The aims of this study were to investigate the relationship of hematologic parameters and Alberta Stroke Programme Early Computed Tomography Score (ASPECTS) with mortality in geriatric patients suffering an ischemic stroke. Materials and Methods: Geriatric ischemic stroke patients referring to Mehmet Akif Inan Training and Research Hospital between May 2016-May 2019 were retrospectively analyzed. ROC curve analysis was performed to determine the predictive value of hematologic parameters with respect to in-hospital mortality. Multivariate logistic regression analysis was performed to determine the independent predictors of in-hospital mortality. Results: The neutrophil count, monocyte count, red cell distribution width, neutrophil-lymphocyte ratio and monocyte-lymphocyte ratio were significantly higher, whereas the lymphocyte count was significantly lower in patients who died in the hospital than in those who did not. The areas under the curve for the red cell distribution width, neutrophil-lymphocyte ratio, and monocyte-lymphocyte ratio were 0.720, 0.643, and 0.660, respectively. In the logistic regression analysis, age, female sex, ASPECTS, monocyte-lymphocyte ratio and red cell distribution width were identified as independent predictors of in-hospital mortality. In the correlation analysis, a weak negative correlation was found between the ASPECTS and the red cell distribution width (r = 0.303, p < 0.001). Conclusion: The red cell distribution width, monocyte-lymphocyte ratio, and ASPECTS are independent predictors of in-hospital mortality in geriatric ischemic stroke patients. There is a correlation between the ASPECTS and the red cell distribution width.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".