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Record W3000562243 · doi:10.1158/0008-5472.can-19-2850

The Risk of Ovarian Cancer Increases with an Increase in the Lifetime Number of Ovulatory Cycles: An Analysis from the Ovarian Cancer Cohort Consortium (OC3)

2020· article· en· W3000562243 on OpenAlexaff
Britton Trabert, Shelley S. Tworoger, Katie M. O’Brien, Mary K. Townsend, Renée T. Fortner, Edwin S. Iversen, Patricia Hartge, Emily White, Pilar Amiano, Alan A. Arslan, Leslie Bernstein, Louise A. Brinton, Julie E. Buring, Laure Dossus, Gary E. Fraser, Mia M. Gaudet, Graham G. Giles, Inger Torhild Gram, Holly R. Harris, Judith Hoffman Bolton, Annika Idahl, Michael E. Jones, Rudolf Kaaks, Victoria A. Kirsh, Synnøve F. Knutsen, Marina Kvaskoff, James V. Lacey, I‐Min Lee, Roger L. Milne, N. Charlotte Onland‐Moret, Kim Overvad, Alpa V. Patel, Ulrike Peters, Jenny N. Poynter, Elio Ríboli, Kim Robien, Thomas E. Rohan, Dale P. Sandler, Catherine Schairer, Leo J. Schouten, Veronica Wendy Setiawan, Anthony J. Swerdlow, Ruth C. Travis, Antonia Trichopoulou, Piet A. van den Brandt, Kala Visvanathan, Lynne R. Wilkens, Alicja Wolk, Anne Zeleniuch‐Jacquotte, Nicolas Wentzensen

Bibliographic record

VenueCancer Research · 2020
Typearticle
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsPublic Health Ontario
FundersNational Cancer InstituteNational Heart, Lung, and Blood InstituteCancer Council VictoriaMutuelle Générale de l'Education NationaleInstitut Gustave-RoussyU.S. Department of DefenseWorld Health OrganizationEuropean CommissionNational Institute of Environmental Health SciencesInstitut National de la Santé et de la Recherche MédicaleMedical Research CouncilSwedish Cancer FoundationNational Institutes of HealthU.S. Department of Health and Human ServicesOffice of Dietary SupplementsLigue Contre le CancerNational Institute for Health and Care ResearchNational Health and Medical Research CouncilAmerican Cancer SocietyCancer Research UK
KeywordsOvarian cancerCancerMedicineCohortOncologyGynecologyInternal medicine

Abstract

fetched live from OpenAlex

Repeated exposure to the acute proinflammatory environment that follows ovulation at the ovarian surface and distal fallopian tube over a woman's reproductive years may increase ovarian cancer risk. To address this, analyses included individual-level data from 558,709 naturally menopausal women across 20 prospective cohorts, among whom 3,246 developed invasive epithelial ovarian cancer (2,045 serous, 319 endometrioid, 184 mucinous, 121 clear cell, 577 other/unknown). Cox models were used to estimate multivariable-adjusted HRs between lifetime ovulatory cycles (LOC) and its components and ovarian cancer risk overall and by histotype. Women in the 90th percentile of LOC (>514 cycles) were almost twice as likely to be diagnosed with ovarian cancer than women in the 10th percentile (<294) [HR (95% confidence interval): 1.92 (1.60-2.30)]. Risk increased 14% per 5-year increase in LOC (60 cycles) [(1.10-1.17)]; this association remained after adjustment for LOC components: number of pregnancies and oral contraceptive use [1.08 (1.04-1.12)]. The association varied by histotype, with increased risk of serous [1.13 (1.09-1.17)], endometrioid [1.20 (1.10-1.32)], and clear cell [1.37 (1.18-1.58)], but not mucinous [0.99 (0.88-1.10), P-heterogeneity = 0.01] tumors. Heterogeneity across histotypes was reduced [P-heterogeneity = 0.15] with adjustment for LOC components [1.08 serous, 1.11 endometrioid, 1.26 clear cell, 0.94 mucinous]. Although the 10-year absolute risk of ovarian cancer is small, it roughly doubles as the number of LOC rises from approximately 300 to 500. The consistency and linearity of effects strongly support the hypothesis that each ovulation leads to small increases in the risk of most ovarian cancers, a risk that cumulates through life, suggesting this as an important area for identifying intervention strategies. SIGNIFICANCE: Although ovarian cancer is rare, risk of most ovarian cancers doubles as the number of lifetime ovulatory cycles increases from approximately 300 to 500. Thus, identifying an important area for cancer prevention research.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.044
GPT teacher head0.381
Teacher spread0.338 · 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.

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

Citations60
Published2020
Admission routes1
Has abstractyes

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