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Record W4387203851 · doi:10.1016/j.lana.2023.100598

Colposcopy referral rates post-introduction of primary screening with human papillomavirus testing: evidence from a large British Columbia cohort study

2023· article· en· W4387203851 on OpenAlexafffundabout
Anna Gottschlich, Lovedeep Gondara, Laurie Smith, Jennifer Joy Anderson, Darrel Cook, Mel Krajden, Marette Lee, Ruth Elwood Martin, Joy Melnikow, Stuart Peacock, Lily Proctor, Gavin Stuart, Eduardo L. Franco, Dirk van Niekerk, Gina Ogilvie

Bibliographic record

VenueThe Lancet Regional Health - Americas · 2023
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsMcGill UniversityCanadian Centre for Applied Research in Cancer ControlBC Cancer AgencySimon Fraser UniversityBC Centre for Disease ControlUniversity of British ColumbiaWomen's Health Research Institute
FundersNational Cancer InstituteCanadian Institutes of Health ResearchNational Institutes of HealthMichael Smith Health Research BC
KeywordsColposcopyReferralMedicineTriageCervical screeningCohortCervical cancerObstetricsGynecologyPopulationCytologyCohort studyAscus (bryozoa)Hazard ratioCancerFamily medicineInternal medicineConfidence intervalEmergency medicinePathology

Abstract

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Background: Shifting from cytology to human papillomavirus (HPV)-based cervical cancer screening will initially increase colposcopy referrals. The anticipated impact on health systems has been raised as a concern for implementation. It is unclear if the higher rate of colposcopy referrals is sustained after initial HPV-based screens or reverts to new lower baselines due to earlier detection and treatment of precancer. This study aimed to investigate long-term rates of colposcopy referrals after participation in HPV-based screening. Methods: Participants of HPV for Cervical Cancer Screening trial (HPV FOCAL) received one (HPV1, N = 6204) or two (HPV2, N = 9540) HPV-based screens. After exit, they returned to British Columbia's (BC) cytology screening program. A comparison cohort from the BC screening population (BCS, N = 1,140,745) was identified, mirroring trial inclusion criteria. All participants were followed for 10-14 years through the provincial screening registry. Colposcopy referral rates per 1000 screens were calculated for each group. Trial colposcopy referrals for HPV1 and HPV2 were calculated under two referral scenarios: (1) all HPV positive referred to colposcopy; (2) cytology triage with ASCUS or greater referred to colposcopy. Colposcopy referrals from post-trial screens in HPV1 an HPV2 and all screens in BCS were based on actual recommendations from the screening program. A multivariable flexible survival regression model compared hazard ratios (HR) throughout follow-up. Findings: Scenario 2 referral rates were higher during initial HPV screen(s) vs cytology screen (HPV1: 28 per 1000 screens (95% CI: 24, 33), HPV2: 32 per 1000 screens (95% CI: 29, 36), BCS: 8 per 1000 screens (95% CI: 8.9)). However, post-trial rates in HPV1 and HPV2 were significantly lower than in BCS. Cumulative rates in HPV1 and HPV2 approached the cumulative rate in BCS 11-12 years after HPV-based screening (HPV1: 11 per 1000 screens (95% CI: 10, 12), HPV2: 16 per 1000 screens (95% CI: 15-17), BCS: 11 per 1000 screens (95% CI: 10, 11)). Adjusted models demonstrated reductions in referral rates in HPV1 (HR = 0.6, 95% CI: 0.5, 0.7) and HPV2 (HR = 0.7, 95% CI: 0.6, 0.8) relative to BCS by 54 and 72 months post-final HPV screen respectively. Interpretation: Reduced colposcopy referral rates were observed after initial rounds of HPV-based screening. After initial HPV screening, referral rates to colposcopy after cytology triage were below the current rates seen in a centralized cytology program after approximately four years. Any expected increase in referrals at initiation of HPV-based screening could be countered by staged program implementation. Funding: This work was supported by the National Institutes of Health (R01 CA221918), Michael Smith Health Research BC (RT-2021-1595), and the Canadian Institutes of Health Research (MCT82072).

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.170
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.217
GPT teacher head0.437
Teacher spread0.219 · 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 teacher head, 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

Citations9
Published2023
Admission routes3
Has abstractyes

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