Rethinking the Appraisal and Approval of Drugs for Fracture Prevention
Bibliographic record
Abstract
Background In January 2014, the EMA’s Pharmacovigilance Risk Assessment Committee recommended that strontium ranelate no longer be used for osteoporosis. However, EMA’s Committee for Medicinal Products for Human Use decided to restrict its use rather than ban it. Starting from this fact, evidence of drugs for fracture prevention over the last 30 years was reviewed and lessons to be learnt from this story are highlighted. Findings The general belief that drug therapy may become a “solution” for fragility fractures is challenged. The key points of the article are as follows: Lessons 1 to 5: Bone density and morphometric vertebral compression are not reliable surrogate endpoints. In fact, clinically relevant endpoints are essential to assess harms and benefits in clinical trials. There is a need for assessing overall harm-benefit with well-designed trials, taking into account that drug therapy may not be more effective in high-risk patients. Lessons 6 to 10: While bisphosphonates and strontium ranelate show a questionable harm-benefit ratio on hip fracture prevention, denosumab results are inconclusive and no benefit has been proved coming from calcitonines or teriparatide. After decades of widespread use, effectiveness of drugs for osteoporosis remains uncertain, yet adverse effects are more apparent. Conclusions Well-designed and large trials over prolonged follow-up periods, measuring clinically relevant outcomes as hip and other disabling fractures, are urgently needed in order to properly understand the harm-benefit ratio of commonly prescribed drugs. Regulatory agencies should be more transparent and make individual-patient data from all clinical trials publicly available, allowing for independent assessment
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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 | 0.226 | 0.455 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.025 | 0.016 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.025 | 0.032 |
| Insufficient payload (model declined to judge) | 0.008 | 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".