Validation of A Somatic Maturity Prediction Model in North America and Development of Original Japanese Model with Ogi Growth Study
Bibliographic record
Abstract
Objective: While peak height velocity age (PHVA) forecasting models exist for Westerners, there are no equations that target the Japanese. This study aimed to analyze the suitability of Canadian equations model using data from a large-scale study of Japanese participants to verify their applicability (study 1) and to create model equations that are optimal for Japanese participants by multiple regression analysis using the same data (study 2). Methods and Materials: In Study 1, 3,211 and 2,611 data points from boys and girls, respectively, were used to analyze the fit of Asian data to the sex-specific regression equations developed by Mirwald et al. (2002) and Moore et al. (2015). The participants were used in Study 2 to create an optimal maturity prediction model for the Japanese population, and the applicability of the model was verified. In addition, to verify the external validity of the Maturity prediction model, the data were randomly divided for analysis and for validation prior to the creation of the model equation. Results: The results of Study 1 revealed that previous prediction models were underestimated PHVA for Japanese individuals of both males and females at younger ages and overestimated PHVA at older ages. Thus, it is suggested that the Moore model might not be suitable for the Japanese population. However, by using Study 2, we confirmed that our PHVA prediction model was suitable. Conclusions: The development of a predictive model suitable for the Japanese population through this study may assist in the establishment of optimal training prescriptions and environments during the growth and development period.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".