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Record W2024159061 · doi:10.1038/sj.bjc.6602505

Second primary malignancies in patients with male breast cancer

2005· article· en· W2024159061 on OpenAlexaff
K. Hemminki, Ghislaine Scélo, Paolo Boffetta, Lene Mellemkjær, Elizabeth Tracey, A Andersen, David Brewster, ­Eero Pukkala, Maureen McBride, Erich V. Kliewer, KS Chia, Vera Pompe‐Kirn, Carmen Martos, Jón G. Jónasson, Xinjun Li, Paul Brennan

Bibliographic record

VenueBritish Journal of Cancer · 2005
Typearticle
Languageen
FieldMedicine
TopicMale Breast Health Studies
Canadian institutionsUniversity of ManitobaCancerCare Manitoba
FundersNational Cancer Institute
KeywordsMedicineBreast cancerCancerOncologyInternal medicineMale breast cancerProstateRectumIncidence (geometry)Pancreatic cancerProstate cancerGynecology

Abstract

fetched live from OpenAlex

An international multicentre study of first and second primary neoplasms associated with male breast cancer was carried out by pooling data from 13 cancer registries. Among a total of 3409 men with primary breast cancer, 426 (12.5%) developed a second neoplasia; other than breast cancer, a 34% overall excess risk of second primary neoplasia, affecting the small intestine (standardised incidence ratio, 4.95, 95% confidence interval, 1.35-12.7), rectum (1.78, 1.20-2.54), pancreas (1.93, 1.14-3.05), skin (nonmelanoma, 1.65, 1.16-2.29), prostate (1.61, 1.34-1.93) and lymphohaematopoietic system (1.63, 1.12-2.29). A total of 225 male breast cancers was recorded after cancers other than breast cancer, but an increase was found only after lymphohaematopoietic neoplasms. BRCA2 (and to some extent BRCA1) mutations may explain the findings for pancreatic and prostate cancers. Increases at other sites may be related to unknown factors or to chance. This large study shows that the risks for second discordant tumours after male breast cancer pose only a moderate excess risk.

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)0.003
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.006
GPT teacher head0.252
Teacher spread0.246 · 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

Citations69
Published2005
Admission routes1
Has abstractyes

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Same venueBritish Journal of CancerSame topicMale Breast Health StudiesFrench-language works237,207