Neutrophil Lymphocyte Ratio as a predictor of systemic inflammation - A cross-sectional study in a pre-admission setting.
Bibliographic record
Abstract
BACKGROUND: Neutrophil:lymphocyte ratio (NLR) is an emerging biomarker that is used to predict postoperative mortality and morbidity in cardiac and cancer surgeries. The association of this biomarker with systemic illness and its usefulness in risk assessment of preoperative patients has not been fully elucidated. OBJECTIVES: To determine the prevalence of elevated NLR in preoperative patients and to examine the relationship between elevated NLR and the presence of systemic illnesses as well as anaesthesia risk indices such as American Society of Anesthesia (ASA) and the revised cardiac risk index (RCRI) scores. DESIGN: Cross-sectional study Setting: Anaesthesia pre-admission clinic, Toronto Western Hospital, Toronto, Canada Patients: We evaluated 1117 pre-operative patients seen at an anesthesia preadmission clinic. RESULTS: NLR was elevated (>3.3) in 26.6% of target population. In multivariate analysis, congestive cardiac failure, diabetes mellitus and malignancy were independent risk factors predicting raised NLR. After regression analysis, a relationship between NLR and ASA score (Odds Ratio 1.78; 95% CI: 1.42-2.24) and revised cardiac risk index (RCRI, odds ratio 1.33; 95% CI: 1.09-1.64, p-value: 0.0063) was observed. CONCLUSIONS: NLR was elevated (> 3.3) in 26.6% of patients. Congestive cardiac failure and malignancy were two constant predictors of elevated NLR at >3.3 and > 4.5. There was a strong association between NLR and anesthesia risk scoring tools of ASA and RCRI.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".