Ageism vs the technical imperative applying the GRADE framework to the evidence on hemodialysis in very elderly patients
Bibliographic record
Abstract
PURPOSE: Treatment intensity for elderly patients with end-stage renal disease has escalated beyond population growth. Ageism seems to have given way to a powerful imperative to treat patients irrespective of age, prognosis, or functional status. Hemodialysis (HD) is a prime example of this trend. Recent articles have questioned this practice. This paper aims to identify existing pre-synthesized evidence on HD in the very elderly and frame it from the perspective of a clinician who needs to involve their patient in a treatment decision. PATIENTS AND METHODS: A comprehensive search of several databases from January 2002 to August 2012 was conducted for systematic reviews of clinical and economic outcomes of HD in the elderly. We also contacted experts to identify additional references. We applied the rigorous framework of decisional factors of the Grading of Recommendation, Assessment, Development and Evaluation (GRADE) to evaluate the quality of evidence and strength of recommendations. RESULTS: We found nine eligible systematic reviews. The quality of the evidence to support the current recommendation of HD initiation for most very elderly patients is very low. There is significant uncertainty in the balance of benefits and risks, patient preference, and whether default HD in this patient population is a wise use of resources. CONCLUSION: Following the GRADE framework, recommendation for HD in this population would be weak. This means it should not be considered standard of care and should only be started based on the well-informed patient's values and preferences. More studies are needed to delineate the true treatment effect and to guide future practice and policy.
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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 | 0.298 | 0.575 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.025 |
| Bibliometrics | 0.025 | 0.012 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".