2017 American College of Rheumatology Guideline for the Prevention and Treatment of Glucocorticoid‐Induced Osteoporosis
Bibliographic record
Abstract
OBJECTIVE: To develop recommendations for prevention and treatment of glucocorticoid-induced osteoporosis (GIOP). METHODS: We conducted a systematic review to synthesize the evidence for the benefits and harms of GIOP prevention and treatment options. The Grading of Recommendations Assessment, Development and Evaluation methodology was used to rate the quality of evidence. We used a group consensus process to determine the final recommendations and grade their strength. The guideline addresses initial assessment and reassessment in patients beginning or continuing long-term (≥3 months) glucocorticoid (GC) treatment, as well as the relative benefits and harms of lifestyle modification and of calcium, vitamin D, bisphosphonate, raloxifene, teriparatide, and denosumab treatment in the general adult population receiving long-term GC treatment, as well as in special populations of long-term GC users. RESULTS: Because of limited evidence regarding the benefits and harms of interventions in GC users, most recommendations in this guideline are conditional (uncertain balance between benefits and harms). Recommendations include treating only with calcium and vitamin D in adults at low fracture risk, treating with calcium and vitamin D plus an additional osteoporosis medication (oral bisphosphonate preferred) in adults at moderate-to-high fracture risk, continuing calcium plus vitamin D but switching from an oral bisphosphonate to another antifracture medication in adults in whom oral bisphosphonate treatment is not appropriate, and continuing oral bisphosphonate treatment or switching to another antifracture medication in adults who complete a planned oral bisphosphonate regimen but continue to receive GC treatment. Recommendations for special populations, including children, people with organ transplants, women of childbearing potential, and people receiving very high-dose GC treatment, are also made. CONCLUSION: This guideline provides direction for clinicians and patients making treatment decisions. Clinicians and patients should use a shared decision-making process that accounts for patients' values, preferences, and comorbidities. These recommendations should not be used to limit or deny access to therapies.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".