Timing and magnitude of peak height velocity and peak tissue velocities for early, average, and late maturing boys and girls
Bibliographic record
Abstract
Height, weight, and tissue accrual were determined in 60 male and 53 female adolescents measured annually over six years using standard anthropometry and dual-energy X-ray absorptiometry (DXA). Annual velocities were derived, and the ages and magnitudes of peak height and peak tissue velocities were determined using a cubic spline fit to individual data. Individuals were rank ordered on the basis of sex and age at peak height velocity (PHV) and then divided into quartiles: early (lowest quartile), average (middle two quartiles), and late (highest quartile) maturers. Sex- and maturity-related comparisons in ages and magnitudes of peak height and peak tissue velocities were made. Males reached peak velocities significantly later than females for all tissues and had significantly greater magnitudes at peak. The age at PHV was negatively correlated with the magnitude of PHV in both sexes. At a similar maturity point (age at PHV) there were no differences in weight or fat mass among maturity groups in both sexes. Late maturing males, however, accrued more bone mineral and lean mass and were taller at the age of PHV compared to early maturers. Thus, maturational status (early, average, or late maturity) as indicated by age at PHV is inversely related to the magnitude of PHV in both sexes. At a similar maturational point there are no differences between early and late maturers for weight and fat mass in boys and girls.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".