Aging veterans and the end‐stage renal disease management dilemma in the millennium
Bibliographic record
Abstract
The population of aging veterans with complex multiple medical problems is increasing steadily in developed nations. The life expectancy in an aging population with end-stage renal disease (ESRD) is often compared with terminal malignancy. Renal failure in elderly patients often generates a myriad of complicated issues and the nephrologists are faced with the dilemma of conveying the prognosis of renal failure in elderly patients and also explain the pros and cons of offering a renal replacement therapy. Our objectives were to assess the cumulative survival in veterans with ESRD over 70 years of age and to evaluate the factors considered for either not initiating or withdrawing from dialysis. All veterans above age 70 years, who were being evaluated for possible dialysis therapy over a 5-year period, were included in the study. The cumulative survival rates at 1 year, 3 years and 5 years were 60%, 37%, and 20%, respectively. Tunneled cuffed catheter was the dialysis access in a third of these patients on dialysis adding to the morbidity. Twenty-four patients considered either not initiating or withdrawing from dialysis therapy after consensus agreement from either the patient or the power of attorney. The decision to initiate dialysis therapy should be made considering the social, ethical, and associated comorbid conditions. A decision to not initiate or withdraw dialysis is possible in critically ill elderly patients and if taken judiciously can reduce physical and mental stress of both the patient and their family members.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".