The relationship between laboratory‐based outcome measures and mortality in end‐stage renal disease: A systematic review
Bibliographic record
Abstract
Despite data that traditional laboratory-based outcome measures in dialysis are improving over time, population-based data indicate that mortality rates are not improving in parallel. With increased focus on performance measures based on laboratory-based outcomes (e.g., hematocrit, albumin, and parathyroid hormone), less emphasis has been placed on other markers, some of which may be stronger predictors of mortality. We performed a systematic review to interpret the predictive value of laboratory-based outcome measures in dialysis. We identified studies with data regarding the predictive value of laboratory-based outcomes for mortality in dialysis. We calculated the sample size-weighted pooled relative risk of death with dichotomized "high" vs. "low" levels of each measure. We rank-ordered predictors by scaling the pooled relative risk of each measure by its pooled standard deviation. There were 5171 titles, of which 128 (representing 44 laboratory-based outcomes) were selected. Nine were significantly associated with mortality, in order of decreasing scaled effect size: (1) tumor necrosis factor-alpha, (2) hematocrit, (3) interleukin-6, (4) troponin T, (5) Kt/V(urea), (6) prealbumin, (7) urea reduction ratio, (8) serum albumin, and (9) C-reactive protein. Other oft-cited measures such as calcium phosphate product and parathyroid hormone were not significantly associated with mortality in pooled analysis. Quality improvement efforts to improve traditional laboratory-based outcomes in end-stage renal disease are necessary, but likely insufficient, to improve overall mortality in dialysis. Renewed consideration of cardiovascular, inflammatory, and nutritional markers that are especially strong predictors of mortality may have important implications for risk stratification and targeted therapeutic interventions.
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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 | 0.010 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".