Precision Medicine in the Transition to Dialysis and Personalized Renal Replacement Therapy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Launched in 2016, the overarching goal of the Precision Medicine Initiative is to promote a personalized approach to disease management that takes into account an individual's unique underlying biology and genetics, lifestyle, and environment, in lieu of a one-size-fits-all model. The concept of precision medicine is pervasive across many areas of nephrology and has been particularly relevant to the care of advanced chronic kidney disease patients transitioning to end-stage kidney disease (ESKD). Given many uncertainties surrounding the optimal transition of incident ESKD patients to dialysis and transplantation, as well as the high mortality rates observed during this delicate transition period, there is a pressing urgency for implementing precision medicine in the management of this population. Although the traditional paradigm has been to commence incident hemodialysis patients on a 3 times/week treatment regimen, largely driven by adequacy targets, there has been growing recognition that alternative treatment regimens (ie, incremental hemodialysis) may be preferred among certain subpopulations when taking into consideration factors such as patients' residual kidney function, volume status fluctuations, symptoms, and preferences. In this review, we examine the origins of current practices in how dialysis is initiated among incident ESKD patients; incremental dialysis therapy as a dynamic and patient-centric approach that is tailored to patients' unique characteristics; recent data on the incremental hemodialysis regimen and outcomes; and future research directions using a precision nephrology approach to ESKD management with the potential to develop novel approaches, tools, and collaborative efforts to improve the health, well-being, and survival of this population.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it