Advancements in Osteoporosis Diagnosis, Treatment and Fracture Management
Bibliographic record
Abstract
Fractures are a frequent complication of osteoporosis, often leading to chronic pain, reduced mobility and a lower quality of life. Early detection of osteoporosis is vital, with diagnostic tools such as dual-energy X-ray absorptiometry (DEXA) and the Fracture Risk Assessment Tool (FRAX) providing accurate assessments of bone health and fracture risk. Treatment options continue to evolve, targeting various facets of bone metabolism. Anabolic therapies, such as parathyroid hormone analogs, promote new bone formation, while antiresorptive treatments, including bisphosphonates and monoclonal antibodies, help prevent further bone loss. Emerging combination therapies, such as romosozumab combined with denosumab, show enhanced efficacy in reducing fracture risk. For patients with vertebral fractures, minimally invasive procedures such as vertebroplasty and kyphoplasty can provide pain relief and restore spinal stability. Additionally, specific surgical techniques address special cases, such as sacral fatigue fractures and spinopelvic dissociation. For example, sacroplasty is effective for stabilizing sacral fractures, while spinopelvic triangular stabilization can restore function in complex spinopelvic injuries. Through a comprehensive approach that integrates prevention, advanced diagnostics, personalized treatments and precise surgical interventions, osteoporosis management can significantly improve patient outcomes and quality of life. PEER REVIEWED ARTICLE **Peer reviewers:** Prof. Dr Pedro L. Berjano, MD, PhD, Division of Spine Surgery (GSpine4), IRCCS Ospedale Galeazzi – Sant’Ambrogio, Milan, Italy Prof. Dr Pau Heini, Spine Surgery Department, Klinik Sonnenhof, Bern, Switzerland Prof. Dr Kan Min, Swiss Scoliosis – Centre for Spinal and Scoliosis Surgery, Hirslanden Klinik Im Park, Zurich, Switzerland Critical review by Prof. emer. Dr Max Aebi, University of Bern, Switzerland, and McGill University, Montreal, Canada Received on October 10, 2025; accepted after peer review on November 20, 2025; published on December 12, 2025.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 teacher head, 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".