Low Bone Density in Systemic Sclerosis. A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: The effect of systemic sclerosis (SSc) on bone density is not well understood. Through systematic review of the literature, the objectives of this study were to synthesize data about the prevalence of low bone mineral density (BMD), risk factors for low BMD, and occurrence of fracture and fracture-related mortality in SSc. METHODS: A search was conducted of MEDLINE (1948-2012), Evidence Based Medicine Reviews (1991-2012), EMBASE (1980-2012), and CINAHL (1981-2012). Abstracts were screened to identify studies that evaluated low BMD in patients with SSc. Two investigators independently used a standardized form to abstract prevalence of osteopenia and osteoporosis (OP); risk factors for low BMD, BMD measurements, frequency of fracture, and fracture-related mortality. RESULTS: Screening of 1032 citations identified 19 articles. Fifteen studies compared patients with SSc to controls. Most patients were white, female (prevalence 74%-100%), and postmenopausal (prevalence 45.9%-100%). The prevalence of low BMD and OP was 27%-53.3% and 3%-51.1%, respectively. Ten studies reported a lower BMD in patients with SSc compared to matched controls, whereas 2 studies reported no difference. Candidate risk factors for low BMD in SSc include family history of OP, age, menopause, diffuse subtype, presence of internal organ involvement, low vitamin D levels, and calcinosis. However, the studies supporting these factors were conflicting. Fracture rate ranged between 0% and 38%. No study reported OP-related fracture mortality. CONCLUSION: The data suggest that patients with SSc are at risk of low BMD and fracture, especially when other risk factors for OP are present. The interaction of SSc manifestations, traditional OP risk factors, and clinically relevant outcomes is complex and warrants further research.
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 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".