Osteoporosis treatments and adverse events
Bibliographic record
Abstract
PURPOSE OF REVIEW: Osteoporosis treatments will be used with increasing frequency as the population ages; however, relatively little is known about their long-term safety. Recent case reports cite a range of potential adverse events. We review data regarding atrial fibrillation, bone pain, osteonecrosis of the jaw (ONJ), atypical fractures, and osteosarcoma. RECENT FINDINGS: Incidence of bisphosphonate-related ONJ in osteoporosis patients is unclear, but several studies suggest rates may be higher than one in 100,000. Severe bone pain and esophageal cancer have been described among bisphosphonate users, but their relationship has not been carefully studied. The relationship between atrial fibrillation and bisphosphonates is unclear based on existing data, but the Food and Drug Administration's (FDA) analyses suggest no clear association. Although several case series discuss atypical fractures associated with bisphosphonate use, one epidemiologic study found no association. Finally, one case of osteosarcoma has been reported in a woman using teriparatide. One case in over 200,000 users suggests no increase in risk beyond background risk, but further evaluation is necessary. SUMMARY: Although case reports of adverse events with osteoporosis medications suggest potential links, epidemiological analyses have largely failed to illuminate a strong, clear link between osteoporosis therapies and many adverse events, with ONJ an exception. Until further data are available, providers should be aware of these potential side effects, and inform their patients accordingly.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".