A blueprint for bone health across the lifespan: engaging novel team members to influence fracture rates
Bibliographic record
Abstract
Physical activity is a key, and increasingly recognised lifestyle determinant of bone health. For the mildly sceptical reader, we remind you that 26% of adult bone mass is laid down in two growing years around puberty,1 resistance training mitigates a proportion of postmenopausal bone loss and Cochrane systematic reviews conclude that strength and balance training reduce falls by 35% (and thus, fall-related fractures) in older people. A 2008 BMJ cover article emphasised that preventing falls was key in the battle against fractures. The term, ‘fall-related’ is a more accurate primary label than ‘osteoporotic’ for appendicular fractures among older people. And 80% of fractures occur in those who do not have osteoporosis.2 Nihilism has no place when physical activity is a proven therapeutic agent. But there is also no point in adding to the long list of cross-sectional or athlete studies showing that exercise is associated with greater bone mass as measured by dual-energy x-ray absorptiometry (DXA). There are many great options and we admire the body of brilliant work in bone science and activity going on all over the globe. Here, we respectfully outline a few possible next steps in the spirit of sharing, capacity building and teamwork. Think Wikinomics rather than Moses' tablets. There has been a rightful emphasis on randomised controlled trials (RCTs) (and systematic reviews) with the wisdom of evidence-based practice. It behooves us to recall that well-executed cohort studies complement RCTs. The Study of Osteoporotic Fractures and EPIDOS are just two of many wonderful bone cohort studies that point to mechanisms, address the inter-related determinants that are a factor of real life and provide a great opportunity for …
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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 | 0.025 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.048 | 0.020 |
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".