Psychiatric morbidity and cervical cancer screening: a retrospective population-based case–cohort study
Bibliographic record
Abstract
<h3>Background:</h3> Cervical cancer screening reduces disease-specific mortality. This study aimed to estimate whether bipolar disorder or schizophrenia is associated with disparities in cervical cancer screening rates. <h3>Methods:</h3> This was a retrospective population-based matched case–cohort study of community-dwelling women aged 19–69 in Ontario using linked health administrative databases. We used odds ratios (ORs), hazards ratios and rate ratios (RRs) adjusted for demographic characteristics and relevant comorbidities to compare cervical cancer screening outcomes between women with a diagnosis of bipolar disorder or schizophrenia to women without that history matched on key demographic characteristics, between 2003 and 2015. <h3>Results:</h3> In total, 1 245 457 women were identified for inclusion in the analyses, 119 948 with a diagnosis of bipolar disorder or schizophrenia, and 1 125 509 without. Over a median follow-up duration of 12.5 years, women with the exposure were 36% less likely to be screened (OR 0.64, 95% confidence interval [CI] 0.64–0.65) than those without, and they took longer to undergo screening (median 18.98 mo v. 16.63 mo; χ<sup>2</sup> = 3718.2, <i>p</i> < 0.001). They were also screened less frequently (median 6.16 yr v. 4.69 yr per screen; RR 0.85, 95% CI 0.84–0.85). These effects were consistent after we excluded the 86 475 women (6.9%) with suspected major depressive disorder, and they were larger for the 59 141 women (4.7%) not attached to a family physician. <h3>Interpretation:</h3> Women with bipolar disorder or schizophrenia were less likely to undergo cervical cancer screening, their screening was delayed, and they were screened at a lower rate compared to women without this psychiatric history. This practice gap suggests a need to further address barriers to screening, including access to a family physician, among women with bipolar disorder or schizophrenia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".