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Record W4366991021 · doi:10.3390/curroncol30050338

Mesenchymal Tumors of the Breast: Fibroblastic/Myofibroblastic Lesions and Other Lesions

2023· review· en· W4366991021 on OpenAlexaffvenue
Riordan Azam, Miralem Mrkonjic, Abha A. Gupta, Rebecca A. Gladdy, Andrea Covelli

Bibliographic record

VenueCurrent Oncology · 2023
Typereview
Languageen
FieldMedicine
TopicSoft tissue tumor case studies
Canadian institutionsPrincess Margaret Cancer CentreHospital for Sick ChildrenSickKids FoundationMount Sinai HospitalUniversity of Toronto
Fundersnot available
KeywordsMesenchymal stem cellMedicinePathologyAdipose tissueBreast cancerCancerInternal medicine

Abstract

fetched live from OpenAlex

Mesenchymal breast tumors are a rare and diverse group of tumors that present some of the most challenging cases for multidisciplinary breast cancer teams. As a result of overlapping morphologies and a lack of large-scale studies on these tumors, practices are often heterogeneous and slow to evolve. Herein, we present a non-systematic review that focuses on progress, or lack thereof, in the field of mesenchymal breast tumors. We focus on tumors originating from fibroblastic/myofibroblastic cells and tumors originating from less common cellular origins (smooth muscle, neural tissue, adipose tissue, vascular tissue, etc.).

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.251
GPT teacher head0.463
Teacher spread0.213 · 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 designNot applicable
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

Citations14
Published2023
Admission routes2
Has abstractyes

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