Serum creatinine level, a surrogate of muscle mass, predicts mortality in peritoneal dialysis patients
Bibliographic record
Abstract
BACKGROUND: In hemodialysis patients, higher serum creatinine (Cr) concentration represents larger muscle mass and predicts greater survival. However, this association remains uncertain in peritoneal dialysis (PD) patients. METHODS: In a cohort of 10 896 PD patients enrolled from 1 July 2001 to 30 June 2006, the association of baseline serum Cr level and change during the first 3 months after enrollment with all-cause mortality was examined. RESULTS: The cohort mean ± SD age was 55 ± 15 years old and included 52% women, 24% African-Americans and 48% diabetics. Compared with patients with serum Cr levels of 8.0-9.9 mg/dL, patients with serum Cr levels of <4.0 mg/dL and 4.0-5.9 mg/dL had higher risks of death {HR 1.36 [95% confidence interval (95% CI) 1.19-1.55] and 1.19 (1.08-1.31), respectively} whereas patients with serum Cr levels of 10.0-11.9 mg/dL, 12.0-13.9 mg/dL and ≥14.0 mg/dL had lower risks of death (HR 0.88 [95% CI 0.79-0.97], 0.71 [0.62-0.81] and 0.64 [0.55-0.75], respectively) in the fully adjusted model. Decrease in serum Cr level over 1.0 mg/dL during the 3 months predicted an increased risk of death additionally. The serum Cr-mortality association was robust in patients with PD treatment duration of ≥12 months, but was not observed in those with PD duration of <3 months. CONCLUSIONS: Muscle mass reflected in serum Cr level may be associated with survival even in PD patients. However, the serum Cr-mortality association is attenuated in the early period of PD treatment, suggesting competing effect of muscle mass versus residual renal function on mortality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".