State of the art in osteoporosis risk assessment and treatment
Bibliographic record
Abstract
Osteoporosis constitutes a major public health problem, through its association with age-related fractures, particularly of the hip, vertebrae, distal forearm, and humerus. Over recent decades, it has evolved from being viewed as an inevitable consequence of ageing, to being recognised as a serious and eminently treatable disease. In this article, we review the literature pertaining to the epidemiology of osteoporosis, associated health burden, approaches to risk assessment and treatment. Although there is some evidence that fracture incidence has reached a plateau, or even started to decline, in the developed world, an ageing population and adoption of westernised lifestyles in transitioning populations is leading to an increasing burden of osteoporosis across the world. Whilst the clinical definition of osteoporosis has been based solely on bone mineral density, the prediction of fracture at the individual level has been improved by consideration of clinical risk factors in tools such as FRAX ® , derived from a greater understanding of the epidemiology of osteoporosis. Such advances in approaches to primary and secondary prevention of fractures, coupled with elucidation of the underlying biology, and the development of a range of highly effective antiosteoporosis medications, have enabled a step change in our ability to prevent osteoporosis-related fractures. However, there remains a substantial disparity between the number of individuals at high fracture risk and number treated globally. Urgent work is needed at the level of health care systems, national and international policy, and in communication with patients and public, to ensure that all patients who should receive treatment for osteoporosis actually do so.
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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".