Simple method for developing percentile growth curves for height and weight
Bibliographic record
Abstract
The present paper demonstrates the ease of use of method I by Preece and Baine ([1978] Ann Hum Biol 5:1-24) in generating smoothed growth curves for both height and weight. Using the National Center for Health Statistics (NCHS) growth curve data, smoothed curves were developed and compared to those produced using the least-squares-cubic-spline method. Based on the lower sum of squares and better fit of shape as indicated by residual examination, it was concluded that the method I curve fitting procedure by Preece and Baine ([1978] Ann Hum Biol 5:1-24) fit centile growth curves for height and weight in 2-18-year-old male and female children as well as, if not better than, the least-squares-cubic-spline method used in developing the 1979 NCHS growth curves. Further, as this paper demonstrates, smoothed curves can be generated on a desktop computer using readily available software (the SOLVER function within Microsoft EXCEL).
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.015 |
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".