CERVICAL CANCER SCREENING RATES AMONG KOREAN WOMEN OF CHILDBEARING AGE WITH SYSTEMIC LUPUS ERYTHEMATOSUS
Bibliographic record
Abstract
PV087 / #47 Poster Topic: AS11 - Epidemiology and Public Health Background/Purpose Women with systemic lupus erythematosus (SLE) face an increased risk of cervical cancer (CC), yet their CC screening rates are reportedly low. This study aimed to examine the CC screening rates and identify potential predictors influencing CC screening uptake in Korean women of childbearing age with SLE. Methods Prevalent cases of women of childbearing age (20-49 years) with SLE were identified using the National Health Insurance Service-National Health Information Database (NHIS-NHID) (2016-2017). Age-matched women without rheumatic diseases, including SLE, seropositive rheumatoid arthritis, and ankylosing spondylitis randomly selected at a 1:5 ratio served as controls. For those eligible for CC screening in 2018-2019, Papanicolaou (Pap) test rates were calculated from NHIS-National Health Screening Database. Logistic regression was used to estimate odds ratios (ORs) for factors associated with CC screening. Results Among 10,981 women with SLE and 54,905 age-matched controls eligible for CC screening, Pap test rate was significantly lower in the SLE group compared with the control group (49.6% vs 52.1%, p ≤ 0.0001). Logistic regression analysis revealed that younger age, lower income, self-employment and medical aid insurance types, and rural residence were associated with lower likelihood of Pap test uptake in both the SLE and control groups. The presence of comorbidities increased the likelihood of Pap test uptake in the control group (OR 1.18, 95% CI 1.13-1.23), but had no significant impact on the SLE group (OR 0.95, 95% CI 0.87-1.04). Conclusions CC screening rates were significantly lower in women with SLE compared with their age-matched controls. To lower the risk of CC in women of childbearing age with SLE, it is crucial to implement strategies that reinforce adherence to national CC screening programs for these women, particularly for those who are younger, have lower incomes, have self-employment and medical aid insurance types, or reside in rural areas.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".