Understanding and Managing Corticosteroid-Induced Osteoporosis
Bibliographic record
Abstract
Glucocorticoids are effective immunosuppressants used in a wide variety of diseases. Their use results in secondary osteoporosis in about 30-50% of chronic glucocorticoid users. Glucocorticoids cause a rapid decline in bone strength within the first 3-6 months mostly due to increased bone resorption by osteoclasts. This is followed by a more gradual loss of bone partly due to decreased osteoblastogenesis and osteoblast and osteocyte apoptosis. The loss of bone strength induced by glucocorticoids is not fully captured by bone mineral density measurements. Other tools such as the trabecular bone score and advanced imaging techniques give insight into bone quality; however, these are not used widely in clinical practice. Glucocorticoid-induced osteoporosis should be seen as a widely preventable disease. Currently, only about 15% of chronic glucocorticoid users are receiving optimal care. Glucocorticoids should be prescribed at the lowest dose and shortest duration. All patients should be counselled on lifestyle measures to maintain bone strength including nutrition and weight-bearing exercise. Pharmacological therapy should be considered for all patients at moderate to high risk of fracture as there is evidence for the prevention of bone loss and fractures with a favourable safety profile. Oral bisphosphonates are the current mainstay of therapy, whereas osteoanabolic agents may be considered for those at highest risk of fracture.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".