Malnutrition‐inflammation‐coronary calcification in pediatric patients receiving chronic hemodialysis
Bibliographic record
Abstract
Malnutrition, inflammation, and renal osteodystrophy parameters with resultant coronary calcification (CC) are associated with increased cardiovascular mortality in adults. Previous pediatric studies demonstrated CC in children but none assessed for an association between inflammation, malnutrition, renal osteodystrophy, and CC. To assess CC, ultrafast computerized tomogram was obtained for 16 pediatric patients (6 females; median age 17.2 years; range 9.1-21.2 years) receiving hemodialysis for >/=2 months. Inflammation was assessed by serum IL-6, IL-8, and C-reactive protein levels on the day of the computerized tomogram scan; nutrition parameters included serum albumin, cholesterol, the body mass index standard deviation score, and normalized protein catabolic rate. Renal osteodystrophy parameters included time-averaged serum calcium, phosphorus, total PTH, and calcitriol/calcium dose. Patients received hemodialysis thrice-weekly; mean single pool Kt/V 1.48+/-0.13; and mean normalized protein catabolic rate 1.27+/-0.17 g/kg/day. Five of 16 patients had CC. Patients with CC were older (19.1+/-2.1 vs. 15.4+/-3.1 months; P=0.03), had longer dialysis vintage (49.4+/-15.3 vs. 17.2+/-10.5 months, P=0.0002), lower serum cholesterol (122+/-17.7 vs. 160.4+/-10.6 mg/dL, P=0.02), and higher phosphorus (9.05+/-1.2 vs. 6.1+/-0.96 mg/dL, P=0.0001). Mean serum albumin and normalized protein catabolic rate did not differ for patients with CC. All patients had elevated IL-6 and IL-8 levels compared with healthy norms; the mean IL-6, IL-8, and C-reactive protein levels were not different in patients with CC. Coronary calcification was prevalent in older children receiving maintenance hemodialysis with a longer dialysis vintage. Worse renal osteodystrophy control and malnutrition (low cholesterol) may contribute to CC development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".