Reasons for the High Cesarean Delivery Rate among Women with Ankylosing Spondylitis: Using the Korean National Health Insurance Database
Bibliographic record
Abstract
OBJECTIVE: Women with ankylosing spondylitis (AS) have reported a higher rate of cesarean births than healthy controls. This study aimed to identify factors associated with cesarean births in women with AS. METHODS: Based on the Korean Health Insurance Review and Assessment Service claims database, the subjects comprised female patients aged 20-49 years old with AS. In total, 1293 deliveries after AS diagnosis were included. A logistic regression analysis was performed to identify factors associated with cesarean births. RESULTS: Among the 1293 deliveries in women with AS, 657 were cesarean and 636 were vaginal deliveries. Compared to vaginal delivery, the women who had cesarean deliveries were older, had a longer disease duration, and had a higher portion of primipara and dispensation of drugs. These factors were associated with a higher risk of cesarean delivery: maternal age (OR 1.08, 95% CI 1.04-1.12), disease duration (OR 1.09, 95% CI 1.03-1.14), and preeclampsia (OR 3.94, 95% CI 1.17-13.32). Further, compared to no drug dispensation, these drugs showed higher risks of cesarean delivery: nonsteroidal antiinflammatory drugs (NSAID; OR 1.64, 95% CI 1.31-2.37), tumor necrosis factor inhibitor (TNFi), disease-modifying antirheumatic drugs (DMARD), or corticosteroids (OR 2.01, 95% CI 1.57-2.58). In the subgroup analysis in primiparas, maternal age, or dispensation of NSAID alone, or TNFi, DMARD, or corticosteroids was associated with a higher risk of cesarean delivery. CONCLUSION: Women with AS showed a higher cesarean delivery rate, influenced by both maternal age and disease-related factors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".