Enhancing a Somatic Maturity Prediction Model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Assessing biological maturity in studies of children is challenging. Sex-specific regression equations developed using anthropometric measures are widely used to predict somatic maturity. However, prediction accuracy was not established in external samples. Thus, we aimed to evaluate the fit of these equations, assess for overfitting (adjusting as necessary), and calibrate using external samples. METHODS: We evaluated potential overfitting using the original Pediatric Bone Mineral Accrual Study (PBMAS; 79 boys and 72 girls; 7.5-17.5 yr). We assessed change in R and standard error of the estimate (SEE) with the addition of predictor variables. We determined the effect of within-subject correlation using cluster-robust variance and fivefold random splitting followed by forward-stepwise regression. We used dominant predictors from these splits to assess predictive abilities of various models. We calibrated using participants from the Healthy Bones Study III (HBS-III; 42 boys and 39 girls; 8.9-18.9 yr) and Harpenden Growth Study (HGS; 38 boys and 32 girls; 6.5-19.1 yr). RESULTS: Change in R and SEE was negligible when later predictors were added during step-by-step refitting of the original equations, suggesting overfitting. After redevelopment, new models included age × sitting height for boys (R, 0.91; SEE, 0.51) and age × height for girls (R, 0.90; SEE, 0.52). These models calibrated well in external samples; HBS boys: b0, 0.04 (0.05); b1, 0.98 (0.03); RMSE, 0.89; HBS girls: b0, 0.35 (0.04); b1, 1.01 (0.02); RMSE, 0.65; HGS boys: b0, -0.20 (0.02); b1, 1.02 (0.01); RMSE, 0.85; HGS girls: b0, -0.02 (0.03); b1, 0.97 (0.02); RMSE, 0.70; where b0 equals calibration intercept (standard error (SE)) and b1 equals calibration slope (SE), and RMSE equals root mean squared error (of prediction). We subsequently developed an age × height alternate for boys, allowing for predictions without sitting height. CONCLUSION: Our equations provided good fits in external samples and provide an alternative to commonly used models. Original prediction equations were simplified with no meaningful increase in estimation error.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.030 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it