Estimating Pediatric Glomerular Filtration Rates in the Era of Chronic Kidney Disease Staging
Bibliographic record
Abstract
With the use of information from a database of pediatric patients with concomitant nuclear GFR and serum creatinine (Cr), estimated GFR equations were derived on the basis of local laboratory methods and population. These formulas then were compared with those recommended by the National Kidney Foundation for estimating GFR in children. For this, their ability to estimate accurately an individual's true GFR and chronic kidney disease stage, identify patients whose true GFR was <60 ml/min per 1.73 m(2), and to identify correctly deterioration in an individual's GFR over time was compared. Next, two methods to estimate GFR in children without the use of height or weight were developed. The first was a height- and weight-independent formula; the second was a novel approach using the Schwartz formula and calculating a Cr cutoff based on age-based estimates of height and GFR level of interest, i.e., <60 ml/min per 1.73 m(2). Our results suggest that if local laboratory constants are derived and a height is known, then the Schwartz formula offers the most accuracy with least mathematical complexity to perform in the clinical setting. If height is not available but the local laboratory constants have been derived, then the British Columbia's Children's Hospital 2 formula is of value; however, in the setting of estimating pediatric renal function in the outpatient laboratory, where neither of these factors is commonly known, an approach whereby a Cr cutoff for a GFR of interest is developed is suggested. Provided are Cr levels that are based on a reference method of Cr measurement to facilitate this approach for the clinician.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".