Derivation and validation of prediction equations for glomerular filtration rate in children
Bibliographic record
Abstract
Current prediction equations for glomerular filtration rate (GFR), the most important measure of renal function, are neither accurate nor precise in children. Clinical data were abstracted from 207 charts of pediatric patients with renal disease attending the Montreal Children's Hospital from 1999-2004, to derive serum creatinine (SCR)-based equations and test their ability to predict iothalamate clearance, a gold standard measure of GFR, using linear regression and the Bayesian Information Criterion. In a separate study, CysC-based equations were derived from data on cystatin C (CysC) previously measured in 103 children between 1999 and 2003. Two SCR equations were derived for patients with or without spina bifida. Mean biases were -0.97 ml/min/1.73m 2 and +1.2 ml/min/1.73m2, respectively. Precision, 95% limits of agreement, and sensitivity for detecting abnormal renal function were superior for the new formulae compared to previously published equations. Two CysC-based equations were derived with or without the inclusion of SCR, which were less biased and more precise than comparison formulae and more sensitive but less specific for detecting GFR < 90 ml/min/1.73m 2. Future studies should evaluate the equations derived here, in different populations of children.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".