Table_3_Global, Regional, and National Burden of Low Bone Mineral Density From 1990 to 2019: Results From the Global Burden of Disease Study 2019.docx
Bibliographic record
Abstract
Objective<p>We aim to explore the global spatial prevalence and temporal trends of the burden of low bone mineral density (LBMD) worldwide, due to a lack of related studies.</p>Design<p>Cross-sectional study.</p>Methods<p>We used data from the Global Burden of Disease Study 2019 to conduct this study. LBMD in the GBD study includes both osteopenia and osteoporosis. The estimation for the prevalence, measured by the summary exposure value (SEV), and burden of LBMD was made in DisMod-MR 2.1, a Bayesian meta-regression tool. Correlation analysis was performed using the Spearman rank order correlation methods. The temporal trends were represented by the estimated annual percentage change (EAPC).</p>Results<p>In 2019, there were 438 thousand deaths and 16.6 million DALYs attributable to LBMD, increasing by 111.1% and 93.8% respectively, compared to that in 1990. From 1990 to 2019, the prevalence of LBMD has decreased worldwide, but has increased in high-income North America. Some countries, such as the United States, Australia, Canada, and China had increased disability and mortality rates of LBMD with time. Countries with low socio-demographic index (SDI) had higher incidence and mortality rate than those with high SDI. The prevalence of LBMD was lower in males, but the attributable disability and mortality were higher in males in all years from 1990 to 2019.</p>Conclusion<p>With population aging, countries worldwide, especially those with low-SDI, will face increasing challenges in reducing the burden attributable to LBMD and osteoporosis. The treatment of osteoporosis has been overlooked in men for a long time. Effective measures are warranted to control the prevalence and burden of LBMD.</p>
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.417 | 0.040 |
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".