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Record W2614166182 · doi:10.2147/bctt.s126341

Invasive lobular carcinoma of the male breast – a systematic review with an illustrative case study

2017· review· en· W2614166182 on OpenAlexaff
Jenna‐Lynn Senger, Scott Adams, Rani Kanthan

Bibliographic record

VenueBreast Cancer Targets and Therapy · 2017
Typereview
Languageen
FieldMedicine
TopicMale Breast Health Studies
Canadian institutionsUniversity of SaskatchewanUniversity of Alberta
Fundersnot available
KeywordsMedicineInvasive lobular carcinomaBreast cancerContext (archaeology)Lobular carcinomaEpidemiologyOncologyCancerSurgical oncologyDiseaseInternal medicinePathologyGynecologyDuctal carcinomaInvasive ductal carcinomaBiology

Abstract

fetched live from OpenAlex

Male breast cancer is rare, comprising only 1% of all mammary cancers; invasive ductal carcinoma is by far the commonest subtype in both men and women. Though lobular breast cancer is the second most common subtype seen in women, such cancers are extremely uncommon in men, and this is likely related to the lack of lobular development in the male breast. Thus, due to the rarity of this subtype among breast cancers, compounded by the overall rarity of breast cancer in men, current understanding of the pathogenesis of this disease and its management is largely derived from case series and extrapolation of information from the larger cohort of female patients. This paper provides a systematic review on invasive lobular carcinoma of the male breast in the context of an illustrative case study. A comprehensive analysis of the National Cancer Institute's Surveillance, Epidemiology, and End Results Data 1973-2013 leading to an exploration of the pathogenesis, epidemiology, clinical presentation, diagnosis, tumor characteristics, and management of lobular breast carcinoma in men is also discussed. Lobular subtype of breast cancer remains an enigmatic elusive disease that needs additional research to unravel its overall pathogenesis and molecular profile to provide insight for improved therapeutic management options.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.198
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.098
GPT teacher head0.389
Teacher spread0.291 · 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 designSystematic review
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

Citations18
Published2017
Admission routes1
Has abstractyes

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