Association Between Clinical Risk Factors and Progression of Chronic Kidney Disease in Children
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Children with chronic kidney disease (CKD) have an increased risk of progression to ESRD. There is a need to identify treatments to slow the progression of CKD, yet there are limited data regarding clinical risk factors that may be suitable targets to slow progression. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We performed a retrospective cohort study using the North American Pediatric Renal Trials and Cooperative Studies CKD database. There were 4166 pediatric subjects with CKD stages II to IV. Disease progression was defined as a GFR on follow-up of <15 ml/min per 1.73 m(2) or termination in the registry because of dialysis or transplantation. We used Kaplan-Meier and Cox proportional hazards methods to describe progression rates and determine factors associated with CKD progression. RESULTS: In the univariate analysis, CKD progression was associated with age, gender, race, primary disease, CKD stage, registration year, hematocrit, albumin, corrected calcium, corrected phosphorus, and use of certain medications. Factors that remained significant in the multivariate analysis were age, primary disease, CKD stage, registration year, hypertension, corrected phosphorus, corrected calcium, albumin, hematocrit, and medication proxies for anemia and short stature. CONCLUSIONS: There are multiple risk factors associated with disease progression in the pediatric CKD population. Factors that may be amenable to intervention include anemia, hypoalbuminemia, hyperphosphatemia, hypocalcemia, hypertension, and short stature. Because of the retrospective nature of our study, confirmation of our results from ongoing prospective studies is warranted before recommending prospective interventional trials.
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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 | 0.004 |
| 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".