The use of intradialytic parenteral nutrition to treat malnutrition: a case study.
Bibliographic record
Abstract
Protein energy malnutrition in dialysis patients has been well-described in the literature. Most malnourished patients with end stage renal disease (ESRD) suffer from a mixed marasmus-kwashiorkor type of malnutrition with loss of both somatic and visceral protein mass. Malnutrition is associated with increased risk of morbidity and mortality. Up to 50% of patients on dialysis have protein energy malnutrition (Mortelmans & Vanholder, 1999). Malnutrition may be under-recognized and under-reported in dialysis patients. Malnutrition may result from inadequate food intake secondary to the uremic condition, nausea, vomiting, loss of appetite, altered taste and other physiologic conditions that impede food intake or metabolism. The usual indices of nutritional assessment--body weight, body mass index (BMI), anthropometrics, etc., may be inaccurate in patients with ESRD, as the results are often skewed by fluid retention. Therefore, we often rely on weight loss, bloodwork, a pre-dialysis low serum potassium, phosphorus and urea, as early signs of a decreased food intake. When patients are malnourished, measures such as oral supplements and/or tube feedings may be used to augment protein and calorie intake. However, when these interventions are inadequate to reverse the malnutrition condition, intradialytic parenteral nutrition (IDPN) should be implemented. Although there is no definite supportive data to show that the use of IDPN improves morbidity and mortality of dialysis patients, there are data to support that IDPN has positive effects on numerous nutritional parameters (Acchiardo, 2000; Capelli et al., 1994; Foulks, 1999; Hiroshige et al., 1998; Ikizler et al., 1995; Korzets et al., 1999; Mortelmans & Vanholder, 1999; Saunders et al., 1999; Smolle et al., 1995). In this article, we will discuss the causes of malnutrition in dialysis patients, the use of IDPN on one of our patients, and the potential complications associated with IDPN.
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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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| 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".