MétaCan
Menu
Back to cohort
Record W2996857806 · doi:10.32768/abc.201964156-160

Breast Cancer with Internal Mammary Node Metastases: A Case Presented in a Tumor Board Session and Decision Making

2019· article· en· W2996857806 on OpenAlexaffabout
Léamarie Meloche‐Dumas, Érica Patocskai, Kerianne Boulva, Moïshe Liberman, Rami Younan

Bibliographic record

VenueArchives of Breast Cancer · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineBreast cancerMetastasisOncologyCancerRadiation therapyDissection (medical)Lymph nodeRadiologyInternal medicineSurgery

Abstract

fetched live from OpenAlex

Background: There are several therapeutic options available for breast cancer treatment, now incorporating innovative targeted molecular therapies. Metastatic breast cancer is usually treated with chemotherapy and/or hormonotherapy. Surgery has not been shown to improve survival. Adjuvant radiotherapy (RT) has been proven to be effective in the treatment of locally advanced breast cancer, reducing locoregional recurrence. The optimal treatment of internal mammary lymph nodes (IMN) metastases remains controversial. Case presentation: A 48-year-old woman was diagnosed with invasive breast cancer with ipsilateral metastases to axillary lymph nodes and a contralateral IMN metastasis. This case was presented twice during the tumor board sessions of the Surgical Oncology Service at the Centre hospitalier de l'Université de Montréal (CHUM), Montréal, Canada. Question: Does the internal mammary chain (IMC) dissection could be used as a treatment approach in breast cancer with IMC metastasis? Conclusion: Internal mammary chain dissection should be discussed in tumor board sessions on a case-by-case basis. There are no strong guidelines on the management of IMN metastasis in breast cancer, but there is growing evidence that these women should be treated with curative intent.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.214
Threshold uncertainty score0.799

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.266
Teacher spread0.261 · 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.

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

Citations1
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueArchives of Breast CancerSame topicBreast Cancer Treatment StudiesFrench-language works237,207