Bibliographic record
Abstract
PURPOSE OF REVIEW: To provide an update of recent and influential studies of implementation research in osteoporosis. RECENT FINDINGS: In recent years, several implementation interventions have been designed and tested to improve osteoporosis screening (primary prevention), increase bone mineral density testing and treatment after a fracture (secondary prevention), and enhance shared decision-making along with long-term medication adherence and persistence. Different forms of care coordination, from multifaceted interventions through labor-intensive and capital-intensive 'fracture liaison services', seem most consistently to improve quality of osteoporosis care. When randomized trials (rather than observational studies) are used to test these various interventions, they are often found to be ineffective, and even when they are effective, the effect sizes are modest and less than had been anticipated by researchers or required by decision-makers. However, even modest effects have been shown in economic analyses to be very cost-effective or cost-saving. SUMMARY: Although osteoporosis implementation research has tended to lag behind other conditions such as coronary disease or diabetes, in recent years, the number, quality, novelty, and rigor of osteoporosis intervention trials have substantially increased - though much more work remains to be done.
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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 | 0.017 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".