Total Nitrogen and Free Amino Acid Losses and Protein Calorie Malnutrition of Hemodialysis Patients: Do They Really Matter?’
Bibliographic record
Abstract
BACKGROUND/AIMS: Protein calorie malnutrition (PCM) in patients on hemodialysis (HD) is multifactorial; however, HD per se induces nutrient losses. The aim of the present study was to characterize the losses of total nitrogen (TN) and free amino acids (FAs) through the dialysate and to determine the relationship between this loss and PCM, food ingestion, and the characteristics of the hemodialyzer in patients on HD. METHODS: In a prospective study, 21 patients submitted to low-flux HD 3 times a week were evaluated within a period of 6 months regarding nutritional status, dietary calorie and protein intake, and losses through the dialysate of TN, FA, and urea nitrogen (UN). The type, surface area and reuses (up to 12) of the dialysis membrane were determined on each occasion, and the adequacy of dialysis was estimated by Kt/V. RESULTS: 50% of the patients were considered malnourished, although the mean protein and energy intakes were similar for the malnourished and nourished patients. Mean TN losses through the dialysate were 16 g/session (60% UN). FA losses varied from 3.8 to 4.2 g/total volume. TN and FA in the dialysate did not differ significantly between malnourished and nourished patients. There was a positive correlation between membrane (polysulfone) area and TN (p <0.05) and ultrafiltrate volume and TN (p < 0.05), and a nonsignificant correlation between reuse of the dialysis membrane and TN. CONCLUSIONS: TN and FA losses through the dialysate were similar for malnourished and non-malnourished patients on chronic HD, thus they do not act as indicators of nutritional status impairment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".