Determinants of Cervical Cancer Screening Patterns Among Women With Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: Women with systemic lupus erythematosus (SLE) are vulnerable to cervical dysplasia due to the persistence of human papillomavirus (HPV) infection. The objective of this cross-sectional retrospective study was to investigate the prevalence of cervical cancer screening according to the American Society for Colposcopy and Cervical Pathology (ASCCP) SLE-specific cervical cancer screening guidelines. We also aimed to identify SLE-specific determinants associated with ASCCP adherence. METHODS: Women aged 21 to 64 years enrolled in our institutional SLE registry were included in the study. The electronic medical record was manually reviewed to determine whether the patient was up to date on screening and which organizational guideline was used, in addition to other clinical variables. Multivariable logistic regression was used to estimate adjusted odds ratios (ORs) for ASCCP-congruent screening for each baseline characteristic. RESULTS: This study included 118 women with SLE; 38% were up to date per ASCCP guidelines, 16% were up to date per non-ASCCP guidelines, and 46% were overdue for screening. Having a gynecologist and being actively treated with immunosuppressant therapies were both associated with an increased odds of being up to date per the ASCCP guidelines, while Hispanic ethnicity was associated with reduced odds. CONCLUSION: Only half of the women with SLE in our study had guideline-congruent cervical cancer screening. Current immunosuppression exposure, rather than SLE disease activity, was associated with an increased odds of being up to date according to ASCCP guidelines. This study suggests the need for increased awareness and consensus among interdisciplinary providers regarding SLE-specific cervical cancer screening.
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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 | 0.000 | 0.003 |
| 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) | 0.002 | 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".