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Record W4206917783 · doi:10.32768/abc.202291119-122

A Rare Case of Lobular Carcinoma in Situ Within a Fibroadenoma

2022· article· en· W4206917783 on OpenAlexaff
Fui Tin Pang, Anat Kornecki, Kalan Lynn, Sze Yuen Lee

Bibliographic record

VenueArchives of Breast Cancer · 2022
Typearticle
Languageen
FieldMedicine
TopicBreast Lesions and Carcinomas
Canadian institutionsSt Joseph's Health CareLawson Health Research InstituteWestern University
Fundersnot available
KeywordsLobular carcinomaFibroadenomaPresentation (obstetrics)MedicineDifferential diagnosisPathologyBreast ultrasoundRadiologyDuctal carcinomaBreast cancerCancerMammographyInternal medicine

Abstract

fetched live from OpenAlex

Background: Fibroadenomas (FAs) are common benign breast tumors which account for 68% of all breast masses. They can be divided into simple and complex FAs. Though unusual, FAs can harbor invasive or in situ carcinomas, as well as high risk lesions such as lobular carcinoma in situ (LCIS) as we present in this case report.Case Presentation: In this case report, we are presenting a rare case of LCIS within a FA with a brief description of presentation, classification and radiological features.Conclusion: Carcinomas within FAs remain a diagnostic challenge. Despite being considered benign, FAs should still be closely monitored by ultrasound (US) as they can still harbor in situ or invasive carcinomas or high-risk lesions, including LCIS. Radiologists should always be aware and consider these as part of their differential diagnosis whenever benign looking masses have atypical features or presentation. The current management of LCIS detected within FA is still similar to LCIS found elsewhere in the breast.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0040.002

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 head0.247
Teacher spread0.236 · 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 designCase report
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

Citations2
Published2022
Admission routes1
Has abstractyes

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