Early withdrawal and non‐withdrawal death in the months following hemodialysis initiation: A retrospective cohort analysis
Bibliographic record
Abstract
INTRODUCTION: Whether and how factors associated with elective hemodialysis withdrawal differ from those associated with non-withdrawal death soon after maintenance hemodialysis initiation have not been well studied. METHODS: A retrospective cohort analysis was performed using USRDS data from 2011 to 2014. Patients were randomly categorized 2:1 into training and validation samples. Elective withdrawal deaths were identified using the Death Notification form. Multinomial logistic regression was used to fit a prediction model for three outcome categories (withdrawal, non-withdrawal death, survival at 6 months) as a function of demographic, comorbidity, and functional status. FINDINGS: The training sample comprised 80,284 hemodialysis patients. Mean age was 71.7 ± 11.4 years, 44.9% were female, 72.9% were white, and 22.8% were black. Within 6 months, 19.1% died, of whom 2099 (2.6%) withdrew and 13,223 (16.5%) died of a non-withdrawal cause; 13.7% of all deaths were withdrawals. Baseline characteristics and event rates were similar among the 40,142 patients in the validation sample. The model was calibrated adequately and could discriminate moderately well between withdrawal and survival (area under ROC curve [AUC]: 0.77) and between non-withdrawal death and survival (AUC: 0.73). However, discrimination between withdrawal and non-withdrawal death was relatively low (AUC: 0.62). Older age and white, compared with non-white, race were each associated with greater odds of death, and these associations were stronger for withdrawal than for non-withdrawal death. DISCUSSION: Advanced age and white, as opposed to black, race were most strongly associated with early elective hemodialysis withdrawal compared with non-withdrawal death. However, it is difficult to differentiate between patients who will experience early withdrawal vs. non-withdrawal death, as many factors are similarly associated with both outcomes.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".