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Record W2980968995 · doi:10.1002/uog.21894

Serial endometrial thickness and risk of non‐endometrial hormone‐dependent cancers in postmenopausal women in <scp>UK</scp> Collaborative Trial of Ovarian Cancer Screening

2019· article· en· W2980968995 on OpenAlexaff
Matthew Burnell, Aleksandra Gentry‐Maharaj, Clara Helene Glazer, Chloe Karpinskyj, Andy Ryan, Sophia Apostolidou, J. Kalsi, Mahesh Parmar, S. Campbell, Ian Jacobs, Usha Menon

Bibliographic record

VenueUltrasound in Obstetrics and Gynecology · 2019
Typearticle
Languageen
FieldMedicine
TopicEndometrial and Cervical Cancer Treatments
Canadian institutionsCReATe Fertility Centre
FundersMedical Research CouncilUniversity College London Hospitals NHS Foundation TrustNational Institute for Health and Care ResearchUniversity College LondonDepartment of Health and Social CareCancer Research UK
KeywordsMedicineEndometrial cancerOncologyGynecologyBreast cancerCancerProportional hazards modelHormone replacement therapy (female-to-male)ObstetricsInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Estrogen is a well-established risk factor for various cancers. It causes endometrial proliferation, which is assessed routinely as endometrial thickness (ET) using transvaginal ultrasound (TVS). Only one previous study, restricted to endometrial and breast cancer, has considered ET and the risk of non-endometrial cancer. The aim of this study was to explore the association between baseline and serial ET measurements and nine non-endometrial hormone-sensitive cancers, in postmenopausal women, using contemporary statistical methodology that attempts to minimize the biases typical of endogenous serial data. METHODS: This was a cohort study nested within the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). In the ultrasound arm of UKCTOCS, 50639 postmenopausal women, aged 50-74, underwent annual TVS examination, of whom 38 105 had a valid ET measurement, no prior hysterectomy and complete covariate data, and were included in this study. All women were followed up through linkage to national cancer registries. The effect of ET on the risk of six estrogen-dependent cancers (breast, ovarian, colorectal, bladder, lung and pancreatic) was assessed using joint models for longitudinal biomarker and time-to-event data, and Cox models were used to assess the association between baseline ET measurement and these six cancers in addition to liver cancer, gastric cancer and non-Hodgkin's lymphoma (NHL). All models were adjusted for current hormone-replacement therapy (HRT) use, body mass index, age at last menstrual period, parity and oral contraceptive pill use. RESULTS: The 38 105 included women had a combined total of 267 567 (median, 8; interquartile range, 5-9) valid ET measurements. During a combined total of 407 838 (median, 10.9) years of follow-up, 1398 breast, 351 endometrial, 381 lung, 495 colorectal, 222 ovarian, 94 pancreatic, 79 bladder, 62 gastric, 38 liver cancers and 52 NHLs were registered. Using joint models, a doubling of ET increased significantly the risk of breast (hazard ratio (HR), 1.21; 95% CI, 1.09-1.36; P = 0.001), ovarian (HR, 1.39; 95% CI, 1.06-1.82; P = 0.018) and lung (HR, 1.25; 95% CI, 1.02-1.54; P = 0.036) cancers. There were no statistically significant associations between ET and the remaining six cancers. CONCLUSION: Postmenopausal women with high/increasing ET on TVS are at increased risk of breast, ovarian and lung cancer. It is important that clinicians are aware of these risks, as TVS is a common investigation. © 2019 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of the International Society of Ultrasound in Obstetrics and Gynecology.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.011
GPT teacher head0.268
Teacher spread0.257 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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