Intensive weight loss combining flexible dialysis with a personalized, ad libitum, coach‐assisted diet program. A “pilot” case series
Bibliographic record
Abstract
UNLABELLED: Obesity is a growing problem on dialysis. The best approach to weight loss has not been established. The risks of malnutrition may offset the advantages of weight loss. Personalized hemodialysis schedules, with an incremental approach, are gaining interest; to date, no studies have explored its potential in allowing weight loss. This case series reports on combining flexible, incremental hemodialysis, and intensive weight loss. SETTING: a small Dialysis Unit, following incremental personalized schedules (2-6 sessions/week, depending on residual function), tailored to an equivalent renal clearance >12 mL/min. Four obese and two overweigh patients (5 male, 1 female; age: 40-63 years; body mass index [BMI] 31.1 kg/m(2)) were enrolled in a coach-assisted weight loss program, with an "ad libitum" approach (3-6 foods/day chosen on the basis of their glycemic index and glycemic load). The diet consists of 8 weeks of rapid weight loss followed by 8-12 weeks of maintenance; both phases can be repeated. This study measures weight loss, side effects, and patients' opinions. Over 12-30 months, all patients lost weight (median -10.3 kg [5.7-20], median ΔBMI-3.2). Serum albumin (pre-diet 3.78; post-diet 3.83 g/dL), hemoglobin (pre-diet 11; post-diet 11.2 g/dL), and acid-base balance (HCO(3) pre-diet: 23.3; post-diet: 23.4 mmol/L) remained stable, with decreasing needs for erythropoietin and citrate or bicarbonate supplements. Calcium-phosphate-parathyroid hormone (PTH) balance improved (PTH-pre 576; post 286 pg/mL). Three out of 4 hypertensive patients discontinued, 1 decreased antihypertensives. None experienced severe side effects. Patient satisfaction was high (9 on a 0-10 analog scale). Personalized, incremental hemodialysis schedules allow patient enrollment in intensive personalized weight loss programs, with promising results.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".