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Record W2104615333 · doi:10.1093/jnci/djt465

Anthropometric and Hormonal Risk Factors for Male Breast Cancer: Male Breast Cancer Pooling Project Results

2014· review· en· W2104615333 on OpenAlexaff
Louise A. Brinton, Michael B. Cook, Valerie McCormack, Kristina Johnson, Håkan Olsson, John T. Casagrande, Rosie Cooke, Roni T. Falk, Susan M. Gapstur, Mia M. Gaudet, J. Michael Gaziano, Georgios Gkiokas, Pascal Guénel, B. E. Henderson, A. R. Hollenbeck, Ann W. Hsing, L. N. Kolonel, Claudine Isaacs, Jay H. Lubin, Karin B. Michels, Eva Negri, Dominick Parisi, Eleni Petridou, M. C. Pike, Elio Ríboli, Howard D. Sesso, K. Snyder, Anthony J. Swerdlow, D Trichopoulos, Giske Ursin, Piet A. van den Brandt, Stephen K. Van Den Eeden, Elisabete Weiderpass, Walter C. Willett, Marianne Ewertz, D. B. Thomas

Bibliographic record

VenueJNCI Journal of the National Cancer Institute · 2014
Typereview
Languageen
FieldMedicine
TopicMale Breast Health Studies
Canadian institutionsUniversity of Ottawa
FundersNational Institutes of Health
KeywordsBreast cancerAnthropometryOncologyMedicineCancerHormonePoolingGynecologyInternal medicinePhysiologyObstetricsComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: The etiology of male breast cancer is poorly understood, partly because of its relative rarity. Although genetic factors are involved, less is known regarding the role of anthropometric and hormonally related risk factors. METHODS: In the Male Breast Cancer Pooling Project, a consortium of 11 case-control and 10 cohort investigations involving 2405 case patients (n = 1190 from case-control and n = 1215 from cohort studies) and 52013 control subjects, individual participant data were harmonized and pooled. Unconditional logistic regression generated study design-specific (case-control/cohort) odds ratios (ORs) and 95% confidence intervals (CIs), with exposure estimates combined using fixed effects meta-analysis. All statistical tests were two-sided. RESULTS: Risk was statistically significantly associated with weight (highest/lowest tertile: OR = 1.36; 95% CI = 1.18 to 1.57), height (OR = 1.18; 95% CI = 1.01 to 1.38), and body mass index (BMI; OR = 1.30; 95% CI = 1.12 to 1.51), with evidence that recent rather than distant BMI was the strongest predictor. Klinefelter syndrome (OR = 24.7; 95% CI = 8.94 to 68.4) and gynecomastia (OR = 9.78; 95% CI = 7.52 to 12.7) were also statistically significantly associated with risk, relations that were independent of BMI. Diabetes also emerged as an independent risk factor (OR = 1.19; 95% CI = 1.04 to 1.37). There were also suggestive relations with cryptorchidism (OR = 2.18; 95% CI = 0.96 to 4.94) and orchitis (OR = 1.43; 95% CI = 1.02 to 1.99). Although age at onset of puberty and histories of infertility were unrelated to risk, never having had children was statistically significantly related (OR = 1.29; 95% CI = 1.01 to 1.66). Among individuals diagnosed at older ages, a history of fractures was statistically significantly related (OR = 1.41; 95% CI = 1.07 to 1.86). CONCLUSIONS: Consistent findings across case-control and cohort investigations, complemented by pooled analyses, indicated important roles for anthropometric and hormonal risk factors in the etiology of male breast cancer. Further investigation should focus on potential roles of endogenous hormones.

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)0.021
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.023
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.008
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0020.000
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.124
GPT teacher head0.443
Teacher spread0.319 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations163
Published2014
Admission routes1
Has abstractyes

Explore more

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