If you don't take it – it can't work: the consequences of not being treated or nonadherence to osteoporosis therapy
Bibliographic record
Abstract
Jonathan D Adachi1, Robert G Josse2, R Graham G Russell3,41Division of Rheumatology, Department of Medicine, McMaster University, Hamilton, Ontario, Canada; 2Osteoporosis Center, Division of Endocrinology and Metabolism St. Michael's Hospital, University of Toronto, Toronto, Ontario, Canada; 3Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Science, Oxford University Institute of Musculoskeletal Sciences (The Botnar Research Centre), Nuffield Orthopaedic Center, Oxford, UK; 4The Mellanby Center For Bone Research, Department of Human Metabolism, The University of Sheffield Medical School, Sheffield, UKAbstract: Osteoporosis is a growing problem worldwide, linked to an increasingly aging population. Despite the availability of a wide variety of treatments for osteoporosis, a significant number of patients are either not being prescribed treatment or discontinue therapy as early as 6 months after initiation. The reasons for a lack of adherence are many but poor adherence increases the risk of fracture and, therefore, the disease burden to the patient and society. Results from large-scale, randomized clinical studies have shown that different osteoporosis treatments are efficacious in reducing the risk of fracture. Studies assessing the effects of discontinuing osteoporosis therapies show that some treatments appear to continue to protect patients from the risk of future fracture even when treatment is stopped. However, these trials involve patients who have been compliant with treatment for between 2 and 5 years, a situation not reflective of real-world clinical practice. In reality, patients who discontinue therapy within the first 6 months may never achieve the optimum protection from fracture regardless of which treatment they have been prescribed. Clinicians need to develop management strategies to enable patients to adhere to their treatment. This will ultimately result in better prevention of fracture and a lower burden of disease to society and patients.Keywords: osteoporosis, adherence, treatment, persistence, compliance, fracture
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 | 0.004 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.013 |
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".