SP658DIALYSIS WITHDRAWAL: CAUSE OF MORTALITY ALONG A DECADE (2004-2014)
Bibliographic record
Abstract
INTRODUCTION AND AIMS: More advanced age and comorbidity of patients starting dialysis makes dialysis withdrawal (DW) more frequent. In international registers (Canadian Register, USRDS,⋯), high mortality for this cause (close to 25%) has been recognized for years. In most of these registers, there are not distinction between death caused by DW and death caused by the illness that was the reason of DW. Our aim was to study mortality in our dialysis unit between 2004 and 2014, focusing on the role of DW. METHODS: Retrospectively, we collected mortality data on our dialysis unit for ten years. During this period, 215 patients died. Clinical, biochemical (analytical closer to the date of exitus, one month before as much) and causes of death were analyzed. Death by DW was defined as that which occurred 72 hours after the last dialysis. Cachexia was defined as anorexia, asthenia, reduced intake and progressive weight loss. RESULTS: DW was proposed to 110 (51.2%) patients, 101 (46%) accepted. It was responsible for the death in 23% of cases (23 patients), 10.65% of overall mortality. The others (78 patients) died by the processes that conditioned its withdrawal. Univariate study (table) In the logistic regression analysis, factors associated with DW were: more advanced age (HR:1.04, 95% CI 1.01 to 1.02) , cachexia (HR:3.2, 95% CI 1.2 to 8.6) and higher ferritin levels (HR:1.001, 95% CI 1 to 1.002). SP658 Figure
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".