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Record W4407285753 · doi:10.1093/jcag/gwae059.181

A181 GEOGRAPHIC IMPACT ON SCREENING AND DEVELOPMENT OF CERVICAL NEOPLASIA IN INFLAMMATORY BOWEL DISEASE

2025· article· en· W4407285753 on OpenAlexafffundabout
Quinn Goddard, Stephanie Coward, Cynthia H. Seow, Stefania Bertazzon, Gilaad G. Kaplan

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsInflammatory bowel diseaseMedicineDiseaseCervical carcinomaGastroenterologyInternal medicineCervical cancerCancer

Abstract

fetched live from OpenAlex

Abstract Background While the risk of cancer in inflammatory bowel disease (IBD) is elevated, studies indicate a lower odds of gynecological cancers. Cervical cancer has become relatively preventable as a result of screening programs. However, geographic access impacts screening, possibly resulting in higher rates of cancer in rural areas. Aims To investigate in the IBD population: (1) the odds of cervical neoplasia (squamous intraepithelial neoplasia grade III or cervical cancer) compared to matched controls, (2) screening rates (Pap smears), and (3) impact of urban vs rural residence on these estimates. Methods We conducted a population-based matched cohort study using administrative healthcare databases in Alberta to identify females with IBD (n=22,245), age- and sex-matched 10-to-1 to controls (n=161,070) from fiscal years 2003–2021. The Alberta Cancer Registry provided morphology and diagnosis date for cervical neoplasia. Physician Claims provided Pap smears. Screening rates were defined as Pap smears per person-year (PY), with eligible Pap smears being those received by individuals aged 21–69, as per provincial screening guidelines. The Provincial Registry provided annual geographic data indicating urban vs rural residency. Average annual percentage change (AAPC) in screening and incidence of cervical neoplasia was calculated using Poisson regression. Conditional logistic regression compared cervical neoplasia in cases and controls, reported as odds ratios (ORs) and 95% confidence intervals (CIs), and evaluated rurality as an effect modifier using an interaction term. Two-sample t-tests compared mean Pap smears per PY between urban and rural populations. Results Females with IBD have lower odds of both cervical cancer (OR: 0.65; 95%CI: 0.47, 0.92) and neoplasia (OR: 0.76; 95%CI: 0.68, 0.84) compared to controls, but rural status was not a modifier (p=0.38). Screening rates in those with IBD were not significantly different from controls (p=0.49). Rural individuals with IBD were screened less than their urban counterparts (0.086 vs 0.30 Pap smears per PY, p<0.001), and a similar pattern was observed in controls (0.087 vs 0.29 Pap smears per PY, p<0.001). The proportion of eligible individuals with IBD receiving Pap smears decreased over time (AAPC: −5.15; 95%CI: −5.30, −4.99), while the diagnosis of cervical neoplasia was stable (AAPC: −0.14; 95%CI: −2.03, 1.79). Conclusions Individuals with IBD had lower odds of cervical neoplasia and cancer, regardless of rural vs urban status. Screening was lower for rural individuals in both IBD and non-IBD populations. Although screening rates declined in individuals with IBD, the detection of cervical neoplasia remained stable. Future studies should explore barriers to and timing of screening, especially in rural areas, as well as the reasons for the reduced risk of cervical neoplasia in IBD. Yearly proportion of eligible individuals receiving a Pap smear in the IBD population (top) and yearly rate of cervical neoplasia diagnoses (bottom) in individuals with IBD. Funding Agencies CIHR

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.251
Threshold uncertainty score0.499

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.004
GPT teacher head0.217
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

Explore more

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