Considerations in the nutritional management of patients with acute renal failure
Bibliographic record
Abstract
Despite improvements in medical and dialytic therapies, mortality rates for patients with complicated acute renal failure (ARF) remains tragically high-above 50%. Mortality rates also remain persistently high in patients with ARF and preexisting or hospital-acquired malnutrition. ARF causes significant changes in substrate utilization largely because of the metabolic consequences of acute uremia compounded by underlying stress from acute illness. Alterations in protein or amino acid, carbohydrate, and lipid metabolism as well as fluid, electrolyte, and acid-base balance need to be considered when providing nutritional therapy in patients with ARF. Also, the degree of renal impairment, which influences the need for renal replacement therapy (RRT), impacts nutritional requirements. As medical management is becoming highly aggressive in treating ARF with RRT, the ability to provide adequate nutrition is enhanced; however, no consensus on optimal caloric and macro-/micronutrient requirements is available. More current research is required to clarify nutritional needs of this patient population. Nevertheless, individualizing nutrition care and integrating nutritional therapies within a team setting is essential in providing optimal patient care in the presence of ARF.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".