doi:10.1155/2012/435736 Clinical Study Modifiable Risk Factors for Early Mortality on Hemodialysis
Bibliographic record
Abstract
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Data of incident hemodialysis patients from 2001 to 2007 were abstracted from The Renal Disease Registry (TRDR) from central Ontario, Canada and followed until December 2008 to determine 90-day mortality rates for incident hemodialysis patients. Modifiable risk factors of early mortality were determined by a Cox model. In total, 876 of 4807 incident patients died during their first year on dialysis; 304 (34.7%) deaths occurred within the first 90 days of dialysis initiation. The majority of deaths were attributed to a cardiovascular event or infection and more likely occurred in older patients and those with cardiovascular co-morbidities. Of potentially modifiable risk factors, low body mass index (<18.5), a surrogate for malnutrition, was a strong predictor of early mortality [adjusted hazard ratio (HR) 4.22 (CI: 3.12–5.17)]. Also, central venous catheter use was associated with a 2.40 fold increase risk of death (CI: 1.4–3.90). Patients who attended a multidisciplinary pre-dialysis clinic were less likely to die (HR: 0.60, CI: 0.47–0.78). The first 90 days after initiation of dialysis is a period of especially high risk of death. We have identified potentially modifiable risk factors in vascular access type, pre-dialysis care and nutritional status. 1.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.153 | 0.087 |
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".