Effect of personalized nutritional counseling in maintenance hemodialysis patients
Bibliographic record
Abstract
Monitoring nutritional parameters is an integral part of hemodialysis (HD) patient treatment program. The purpose of this study was to evaluate the impact of the personalized nutritional counseling (PNC) on calcium-phosphorus metabolism, potassium, albumin, protein intake, interdialytic weight gain (IDWG), body composition parameters and fluid overload in HD patients. This was a multicenter longitudinal intervention study with 6 months of follow-up and 731 patients on maintenance HD from 34 dialysis units in Portugal were enrolled. Biochemical and body composition parameters were measured at baseline, 1, 3 and 6 months after the PNC. Patient's mean age was 64.9 (95% confidence interval [CI]: 63.8-66.0) years and mean HD time was 59.8 (95% CI: 55.3-64.3) months. Regarding data comparison collected before PNC vs. 6 months after, we obtained, respectively, the following results: patients with normalized protein catabolic rate (nPCR) ≥ 1 g/kg/day = 66.5% vs. 73.5% (P = 0.002); potassium > 5.5 mEq/L = 52% vs. 35.8% (P < 0.001); phosphorus between 3.5 and 5.5 mg/dL = 43.2% vs. 52.5% (P < 0.001); calcium/phosphorus (Ca/P) ratio ≤ 50 mg/dL = 73.2 % vs. 81.4% (P < 0.001); albumin ≥ 4.0 g/dL = 54.8% vs. 55% (P = 0.808); presence of relative overhydration = 22.4% vs. 25% (P = 0.283); IDWG > 4.5% = 22.3% vs. 18.2% (P = 0.068). PNC resulted in a significant decrease in the prevalence of hyperkalemia, hypophosphatemia and also showed amelioration in Ca/P ratio, nPCR and an increase in P of hyphosphatemic patients. Our study suggests that dietetic intervention contributes to the improvement of important nutritional parameters in patients receiving hemodialysis treatment.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".