C-Reactive Protein and Mortality in Hemodialysis Patients: The Dialysis Outcomes and Practice Patterns Study (DOPPS)
Bibliographic record
Abstract
BACKGROUND: We examined associations of C-reactive protein (CRP) levels with mortality in Japanese hemodialysis patients and trends in prevalence of CRP measurement at hemodialysis facilities internationally. To assess whether measurement of CRP may influence outcomes, we examined associations of facility prevalence of CRP measurement with mortality. METHODS: CRP measurements were from a cross-section of patients in the international Dialysis Outcomes and Practice Patterns Study (n = 610 facilities, 16,355 patients). Cox proportional hazards models assessed associations of mortality with CRP in Japan, and with a facility's frequency of measuring CRP internationally, (except in the USA and Canada). RESULTS: From 2002-2004, CRP was measured in 0-19% of patients in each country, except Japan (55%). From 2005-2007, CRP was measured in ≥ 50% of country patients except in Canada (15%) and the USA (2%). After multivariable adjustment, the hazard ratio (HR) of death was 1.6- to 2.4-fold higher (p < 0.05) for various categories of CRP levels >3 mg/l (vs. <1.0 mg/l). Cardiovascular mortality risk was lower in facilities measuring CRP for ≥ 50% of patients (HR = 0.72, p = 0.01) in multivariable-adjusted analyses. CONCLUSIONS: CRP is informative regarding mortality risk beyond that provided by other inflammatory and nutritional markers, with significantly higher risk seen at CRP >3 mg/l. Greater use of CRP may lead to improved patient care as suggested by the association of greater CRP measurement with lower cardiovascular mortality.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".