EFFECT OF RESISTANCE OR AGILITY OR SHAM EXERCISE TRAINING ON BONE PARAMETERS IN 75???85 YEAR OLD WOMEN WITH LOW BONE MASS
Bibliographic record
Abstract
PURPOSE Bone mass is a major risk factor for fall-related fracture. There have been few RCTs of the effect of exercise on bone mass in women aged > 75 years. METHODS We randomized 104 women aged 75–85 years (mean 79 yrs) who had low bone mass (osteopenia or osteoporosis) into either (i) resistance training, (ii) agility training, or (iii) sham exercise groups. Groups were stratified by use or bisphosphonates or not. Exercise intervention occurred twice weekly for 26 weeks. Resistance training was performed using both Keiser equipment and free weights. Agility training followed a curriculum devised by a physiotherapist (TLA). Sham exercise consisted of stretching and posture-related exercises. Bone mass was measured using a Hologic 4500 DXA scanner. Bone cross-sectional area, trabecular area and bone strength index were assessed at the distal and shaft regions of both the tibia and the radius by peripheral quantitative computerized tomography (pQCT). SUMMARY OF RESULTS Mean aBMD at the total hip at baseline was 0.686 g/cm2 and the mean t-score was −2.08. Analysis using intention-to-treat did not reveal significant differences between groups in aBMD secondary to our intervention. Subanalysis of those participants who were taking bisphosphonates also did not reveal any differences in BMD as measured by DXA or in pQCT measures. CONCLUSION BMD by DXA does not detect group differences after 26-week exercise intervention in older women with low bone mass.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".