Diagnostic challenges in osteoporosis. Indications for bone densitometry and establishing secondary causes.
Bibliographic record
Abstract
OBJECTIVE: To review indications for assessing bone mineral density (BMD) and to review patient characteristics and diseases associated with osteoporosis. QUALITY OF EVIDENCE: This paper is based on data from longitudinal observational studies of how BMD and other risk factors affect development of fragility fractures and on several peer-reviewed publications describing pathophysiology of bone turnover and pathogenesis of osteoporosis. Indications for obtaining BMD and monitoring treatment are based on the recommendations of the Osteoporosis Society of Canada derived from the consensus opinion of a panel of experts in osteoporosis and based on their review of the primary literature. MAIN MESSAGE: Measurement of BMD provides the best single objective predictor of the relative risk of fracture at sites such as the vertebrae, hip, and wrist, predicting the likelihood of fracture with as much accuracy as measurement of elevated blood pressure predicts stroke. In addition to making the diagnosis of osteoporosis, BMD measurements are used to monitor progression of osteoporosis and effects of therapy. At this date, dual energy x-ray absorptiometry is preferred for measuring BMD. The most likely causes of osteoporosis in any patient are age, hormone withdrawal (in both men and women), and drugs (particularly corticosteroids). Secondary causes, particularly hyperparathyroidism and multiple myeloma, should be excluded by performing appropriate laboratory tests. CONCLUSION: A BMD measurement should be obtained for patients at high risk of osteoporosis and fragility fractures to guide initiation and monitor success of therapy.
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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".