The Relationship between Neutrophil–Lymphocyte Ratios with Nutritional Status, Risk of Nutritional Indices, Prognostic Nutritional Indices and Morbidity in Patients with Ischemic Stroke
Bibliographic record
Abstract
Background: In recent years, whole blood parameters and derivatives have been used as prognostic criteria in the course of various diseases. The aim of this study was to evaluate the relationship between parameters such as the neutrophil–lymphocyte ratio (NLR), the systemic immune-inflammation index (SII), the prognostic nutritional index (PNI), controlling nutritional status (CONUT) score, nutritional risk index (NRI) and immunonutrition status and disease activity in patients with ischemic stroke of the small-vessel, large-vessel and other etiologies. Methods: We retrospectively evaluated the records of 1454 consecutive ischemic stroke patients hospitalized in the emergency department of Gaziosmanpasa Education and Research Hospital from 2019 to 2023. Results: Of the 1350 patients with ischemic stroke included in the study, 58.8% had small-vessel disease, 29.3% had large-vessel disease and 11.9% had other etiologies. There was a significant difference between the three etiology groups for PNI and CONUT. The mean of PNI was 47.30 ± 8.06 in the other etiology group, 37.25 ± 7.23 in the small-vessel group, and 34.78 ± 8.16 in the large-vessel disease group. The mean of CONUT was 5.49 ± 1.20 in the small-vessel group, 5.12 ± 1.46 in the large-vessel group and 4.22 ± 1.11 in the other etiology group. In addition, CONUT and PNI were also found to be independent risk factors for mortality. A negative significant correlation was observed between PNI and NLR (r: −0.692), SII (r: −0.591), and CONUT (r: −0.511). Significant correlations were observed between CONUT and NLR (r: 0.402), SII (r: 0.312). Conclusions: PNI, CONUT and NRI were found as more accurate prognostic indicators of nutritional status in patients with ischemic stroke. NLR and SII may be important predictive markers in the course and prognosis of stroke.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 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".