Bibliographic record
Abstract
Antiresorptive drugs, such as amino-bisphosphonates and denosumab (Dmab), have dominated osteoporosis therapies for over 20 years. Since osteoporosis is a chronic disease, antifracture therapy could continue for the rest of a patient's life. Phase III clinical trials for antiresorptive drugs assessed relatively small patient populations for short durations and excluded up to 80% of patients who might seek osteoporosis therapy in clinical practice. Postmarketing reports based upon millions of patient-years and long-term (>5 years) clinical administration have associated some previously unknown, rare adverse events with antiresorptive use including osteonecrosis of the jaw (ONJ) and atypical femur fractures (AFFs). In the osteoporosis patient population, who receive much lower doses of bisphosphonate (BP) or Dmab, the incidence of ONJ is estimated at 0.001% to 0.01%, which is only slightly higher than that seen in the general population. AFFs are insufficiency or fissure transverse fractures originating on the lateral cortex of the subtrochanteric or diaphyseal region of the femur becoming oblique as they progress medially when complete. Incidence rates of AFF range from 1.8/100,000 per year with a 2-year BP exposure to 113/100,000 per year with BP exposure from 8 to 9.9 years. Most recent pathogenic hypotheses of these rare events will be discussed.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".