The association of asthma and its subgroups with osteoporosis: a cross-sectional study using KoGES HEXA data
Bibliographic record
Abstract
BACKGROUND: A few studies have reported the association between asthma and osteoporosis. We aimed to analyze the association of asthma and its subgroups with osteoporosis in the Korean adult population. METHODS: We used the health examinee (HEXA) data from the Korean Genome and Epidemiology Study (KoGES) obtained between 2004 and 2016. We included 162,579 participants (n = 3,160 with asthma; n = 159,419 controls) who reported their previous histories of asthma and osteoporosis. The participants were categorized into 3 groups based on asthma management: participants who did not need further treatment due to controlled symptoms (well controlled); participants with ongoing treatment (being treated); participants who were not treated even though they had symptoms (not being treated). Multiple logistic regression analyses were used to calculate the adjusted odds ratios (aORs) with 95% confidence intervals (CIs) for osteoporosis. Subgroup analyses for age and sex were conducted. RESULTS: The prevalence of osteoporosis was higher in patients with asthma (13.6%) than in controls (6.8%). In the full-adjusted model, the aORs for osteoporosis were 1.74 (95% CI 1.55-1.94, P < 0.001) in patients with asthma compared to controls. There were consistent findings across the age and sex subgroups. The aORs for osteoporosis were 1.43 (95% CI 1.10-1.86, P = 0.008) in the well-controlled asthma group; 1.55 (95% CI 1.28-1.89, P < 0.001) in the being treated asthma group; and 1.96 (95% CI 1.66-2.31, P < 0.001) in the not being treated asthma group compared to the control group. CONCLUSION: Asthma was associated with osteoporosis in the Korean adult population. Patients with asthma not being treated showed the highest ORs for osteoporosis.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".