Anemia and Risk of Hospitalization in Pediatric Chronic Kidney Disease
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Anemia is a well known complication of chronic kidney disease (CKD); however, the prevalence of anemia within CKD stages in the pediatric population has not been established. Additionally, the associated morbidity of anemia in the pediatric CKD population has not been elucidated. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: 2,779 patients ages 2 yr and older in the North American Pediatric Renal Trials and Collaborative Studies database with CKD stage II to V (excluding dialysis or previous transplant patients) were identified. Descriptive statistics and multivariate modeling using logistic regression was performed to determine the prevalence of anemia and to evaluate the correlation between baseline anemia and hospitalization. RESULTS: The prevalence of anemia (hematocrit < 33%) increased from 18.5% in CKD stage II to 68% in CKD stage V (predialysis). Anemic children were 55% more likely to be hospitalized when compared with nonanemic children (odds ratio 1.55; 95% confidence interval 1.23 to 1.94). Similar results were obtained using hematocrit cutoffs of 36 and 39%. CONCLUSIONS: In this pediatric predialysis CKD population, anemia increases with increasing CKD stage and is significantly associated with hospitalization risk. Hematocrit levels above 36 and 39% were not associated with increased risk of hospitalization. Further examination into the effect of correcting anemia on hospitalization rates may provide additional useful information.
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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 | 0.003 |
| 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".