Indicators of lean body mass catabolism: emphasis on the creatinine excretion rate
Bibliographic record
Abstract
BACKGROUND: The major stress response to critical illness leads to a catabolic state and loss of lean body mass. AIMS: To test whether an increased rate of creatinine excretion might provide unique and timely information to monitor cell catabolism; to relate this information to balances of cell constituents (nitrogen, potassium, phosphate and magnesium); to evaluate the effectiveness of nutritional therapy to reverse this catabolic process. DESIGN: Prospective observational study. METHODS: Children with severe traumatic brain injury admitted to the paediatric critical care units of The Hospital for Sick Children, Toronto, Canada and Hospital das Clínicas, Faculty of Medicine of Ribeirão Preto, University of São Paulo, Brazil were studied. Complete 24 h urine collections were obtained for measurement of creatinine excretion rate and daily balances of nitrogen, potassium, phosphate and magnesium. RESULTS: Seventeen patients were studied for 3-10 days. On Day 1, all had negative balances for protein and phosphate. Balances for these intracellular constituents became positive when protein intake was >/=1 g/kg/day and energy intake was >/=50% of estimated energy expenditure (P < 0.0001). Creatinine excretion rate was positively correlated with the urea appearance rate (r = 0.60; P < 0.0001), and negatively with protein balance (r = -0.45; P < 0.0001). Sepsis developed in four patients; before its clinical detection, there were negative balances for all intracellular markers and an abrupt rise in the excretion of creatinine. CONCLUSION: Negative balances of intracellular components and an increase in rate of creatinine excretion heralded the onset of catabolism.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".