Estimating Glomerular Filtration Rate in Children at Serial Follow-up When Height Is Unknown
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Estimation of GFR in children from serum creatinine measures in regional databases is limited by a lack of height data. Furthermore, the ability of GFR estimating equations to quantify changes in GFR over time is unknown. Two methods of estimating GFR when height is unknown and the ability of several GFR equations to quantify GFR changes over time were evaluated. DESIGN, SETTING, PARTICIPANTS & MEASUREMENTS: This retrospective study included 195 children (mean age 11.9 ± 4.6 years, GFR = 78.8 ± 34.5) who underwent iothalamate GFR, serum creatinine, and height measurements; 93 children underwent a second GFR measurement and 47 a third GFR measurement. Four equations were evaluated for bias and precision and for quantifying GFR change over time: (1) Schwartz, using measured height; (2) Schwartz, using estimated height (based on previous height percentile); (3) a locally derived modification of a previously derived height-independent equation. RESULTS: The Schwartz (measured height) displayed the least bias (-2 to +7%), followed by the modified height-independent equation and Schwartz (estimated height). All equations were imprecise. All equations performed similarly at capturing change in measured GFR over time, with no significant difference between estimated and measured GFR percentage change over time. The height-estimated Schwartz formula performed similarly to the height-measured Schwartz in all aspects of equation performance. CONCLUSIONS: Pediatric GFR follow-up studies may be possible using height-independent equations. Estimating height from prior height measurements enhances GFR estimation when height is unknown. These findings will hopefully help advance future pediatric renal function database studies.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".