Opportunistic Screening Using Low‐Dose CT and the Prevalence of Osteoporosis in China: A Nationwide, Multicenter Study
Bibliographic record
Abstract
ABSTRACT Opportunistic screening for osteoporosis can be performed using low-dose computed tomography (LDCT) imaging obtained for other clinical indications. In this study we explored the CT-derived bone mineral density (BMD) and prevalence of osteoporosis from thoracic LDCT in a large population cohort of Chinese men and women. A total of 69,095 adults (40,733 men and 28,362 women) received a thoracic LDCT scan for the purpose of lung cancer screening between 2018 and 2019, and data were obtained for analysis from the China Biobank Project, a prospective nationwide multicenter population study. Lumbar spine (L1–L2) trabecular volumetric bone mineral density (vBMD) was derived from these scans using quantitative computed tomography (QCT) software and the American College of Radiology QCT diagnostic criteria for osteoporosis were applied. Geographic regional differences in the prevalence of osteoporosis were assessed and the age-standardized, population prevalence of osteoporosis in Chinese men and women was estimated from the 2010 China census. The prevalence of osteoporosis by QCT for the Chinese population aged >50 years was 29.0% for women and 13.5% for men, equating to 49.0 million and 22.8 million, respectively. In women, this rate is comparable to estimates from dual-energy X-ray absorptiometry (DXA), but in men, the prevalence is double. Prevalence varied geographically across China, with higher rates in the southwest and lower rates in the northeast. Trabecular vBMD decreased with age in both men and women. Women had higher peak trabecular vBMD (185.4 mg/cm3) than men (176.6 mg/cm3) at age 30 to 34 years, but older women had lower trabecular vBMD (62.4 mg/cm3) than men (92.1 mg/cm3) at age 80 years. We show that LDCT-based opportunistic screening could identify large numbers of patients with low lumbar vBMD, and that future cohort studies are now required to evaluate the clinical utility of such screening in terms of fracture prevention and supporting national health economic analyses. © 2020 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR)..
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".