HIGH-IMPACT EXERCISE AND CORTICAL BONE IN PRE-AND EARLY PUBERTAL GIRLS
Bibliographic record
Abstract
Exercise studies in pre- and early puberty children have shown that impact training has the greatest influence on bone mass at trabecular sites. There is evidence that mechanical loading may increase cortical bone strength mostly through changes in bone geometry and redistribution of bone mass. Therefore, we studied the effect of an eight month, 3x/week, progressive high-impact (weight-bearing) circuit program on tibial cortical area in 10–11 yr old girls (n = 8 exercise, n = 8 non-training controls). Muscle cross-sectional area and bone cortical area (cm2) of the proximal third of the left and right lower leg were measured with a 1.5 T MR system (GE Medical Systems) using a quadrature head coil. The sequence was T1 weighted, spin echo in transverse (tibial) planes, 3.0 mm sections with no gap (ten slices). Tibial cortical area was also subdivided into three anatomical sectors (SI-SIII) with the tibial centroid as an origin. There were no differences in anthropometry, calcium, or physical activity between the training and the control groups at baseline. These parameters changed similarly for both groups over 8 months. The lower limb explosive performance capacity (standing long jump, cm) changed 6% and 4% in the trainees and controls, respectively, but the intergroup difference was not significant. There were no significant post-training differences between the groups in the total, or by sector, muscle or cortical area. The cortical area increased in the left 13% and right leg 11% in the training group. Similar numbers in the controls were 14% and 11% respectively. We conclude that this novel imaging modality does not reveal cortical bone change following an 8-month exercise intervention in 10–11 years old girls.
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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 | 0.000 | 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 | 0.000 | 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) | 0.002 | 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".