Bibliographic record
Abstract
PURPOSE OF REVIEW: With the recent emergence of associations of bisphosphonate therapy with atypical fractures and osteonecrosis of the jaw, there is renewed interest among clinicians and patients for nonpharmacological approaches to bone health. Here, we review the new studies published in the past year or two that advance our knowledge of the effect of mechanical stimuli on bone health. RECENT FINDINGS: Physical activity is associated with serum sclerostin levels; the most physically active individuals have the lowest serum sclerostin levels. Observational trials suggest that physical activity participation results in higher bone mass, but clinical trials suggest that the effects of exercise on areal bone mineral density are small, and vary with the site measured and the type of exercise. Based on current data, it may be best to combine progressive resistance training with interventions such as walking or aerobic dancing if the desire is to improve both spine and hip in postmenopausal women. Low-magnitude high-frequency whole body vibration does not improve bone mineral density and bone structure in postmenopausal women. SUMMARY: Physical activity and exercise are important for the maintenance of musculoskeletal health as we age. Future studies need to investigate the effects of exercise in older populations with rheumatological diseases and those with a history of fragility fractures.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".