Associations of Systemic Immune-Inflammation Index With Mortality Risk Among Adults in Diabetic Kidney Disease, NHANES 1999 to 2018
Bibliographic record
Abstract
Objectives Immune inflammation plays a crucial role in the pathogenesis of diabetic kidney disease (DKD), but an exact assessment of indicators remains undefined. In this study we address the link between systemic immune-inflammation index (SII) and mortality risk in DKD, and we explore the effect of sex disparities. Methods Data from patients with DKD from the National Health and Nutritional Examination Surveys (NHANES, 1999 to 2018) were studied and their causes of death were identified from NHANES-related files. A weighted Cox model was used to evaluate hazard ratios for all-cause, cardiovascular, and cardiocerebrovascular mortality, and these associations were visualized by smoothing curves. Results The average SII was 634.20 (10 3 /μL). There were 1,283 deaths recorded during 273,422 person-months (396 were cardiovascular related and 461 were cardiocerebrovascular related). Higher SIIs in the fifth quintile were significantly associated with increased mortality (p<0.01). SII trends showed an increased risk of all-cause mortality of >697.0 (10 3 /μL), cardiovascular risk of >717.8 (10 3 /μL), and cardiocerebrovascular risk of >650.0 (10 3 /μL). Mortality increased when SII reached 500 to 660 (10 3 /μL) in men and 700 to 760 (10 3 /μL) in women. Conclusions There was a significant association between higher SII and increased risk of all-cause, cardiovascular, and cardiocerebrovascular mortality in DKD patients. In addition, although men had lower SII, their mortality was higher than that of women.
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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 | 0.001 |
| 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 | 0.001 |
| 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".