Tailoring dialysis and resuming low‐protein diets may favor chronic dialysis discontinuation: Report on three cases
Bibliographic record
Abstract
Renal function recovery (RFR), defined as the discontinuation of dialysis after 3 months of replacement therapy, is reported in about 1% of chronic dialysis patients. The role of personalized, intensive dialysis schedules and of resuming low-protein diets has not been studied to date. This report describes three patients with RFR who were recently treated at a new dialysis unit set up to offer intensive hemodialysis. All three patients were females, aged 73, 75, and 78 years. Kidney disease included vascular-cholesterol emboli, diabetic nephropathy and vascular and dysmetabolic disease. At time of RFR, the patients had been dialysis-dependent from 3 months to 1 year. Dialysis was started with different schedules and was progressively discontinued with a "decremental" policy, progressively decreasing number and duration of the sessions. A moderately restricted low-protein diet (proteins 0.6 g/kg/day) was started immediately after dialysis discontinuation. The most recent update showed that two patients are well off dialysis for 5 and 6 months; the diabetic patient died (sudden death) 3 months after dialysis discontinuation. Within the limits of small numbers, our case series may suggest a role for personalized dialysis treatments and for including low-protein diets in the therapy, in enhancing long-term RFR in elderly dialysis patients.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".