Anthropometric Prediction of Total Body Water in Children Who Are on Pediatric Peritoneal Dialysis
Bibliographic record
Abstract
Accurate estimation of total body water (TBW) is a critical component of dialysis prescription in peritoneal dialysis (PD). Gold-standard isotope dilution techniques are laborious and costly; therefore, anthropometric prediction equations that are based on height and weight are commonly used to estimate TBW. Equations have been established in healthy populations, but their validity is unclear in children who undergo PD, in whom altered states of hydration and other confounding alterations in normal physiology, particularly retarded growth and pubertal delay, may exist. TBW was measured by heavy water (H2O18 or D2O) dilution in 64 pediatric patients who were aged 1 mo to 23 yr and receiving chronic PD in the United States and Germany to establish and validate population-specific anthropometric TBW prediction equations and to compare the predictive power of these equations with formulas that have been established in healthy children. The best-fitting equations are as follows: For boys, TBW = 0.10 x (HtWt)0.68 - 0.37 x weight; for girls, TBW = 0.14 x (HtWt)0.64 - 0.35 x weight. The height x weight parameter also predicts body surface area (BSA). These equations can be simplified, with slightly less precision, to the following: For boys, TBW = 20.88 x BSA - 4.29; for girls, TBW = 16.92 x BSA - 1.81. TBW is predicted without systematic deviations and equally well in boys and girls, North American and European, obese and nonobese, growth-retarded and normally sized, and pre- and postpubertal children. In contrast, previous anthropometric equations that were derived from healthy children systematically overpredicted TBW and were less precise in this pediatric PD population. In summary, a new set of anthropometric TBW prediction equations that are suited specifically for use in pediatric PD patients have been provided.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".