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Record W2785301351 · doi:10.1007/s10549-017-4644-3

Is invasion a necessary step for metastases in breast cancer?

2018· review· en· W2785301351 on OpenAlexaff
Steven A. Narod, Victoria Sopik

Bibliographic record

VenueBreast Cancer Research and Treatment · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsUniversity of TorontoPublic Health OntarioWomen's College HospitalToronto Western Hospital
Fundersnot available
KeywordsBreast cancerMedicineCancerOncologyRadiation therapyMetastasisCA15-3Internal medicine

Abstract

fetched live from OpenAlex

PURPOSE: To review the empirical evidence to support the conventional (sequential) model of breast cancer progression, which is based on the paradigm that cancer passes through several stages, including an in situ stage prior to an invasive stage, and thereafter (in some cases) disseminates to the lymph nodes and distant organs. METHODS: We review the cancer literature of the last 50 years which relates to the prevention of invasive breast cancer (through radiotherapy or surgery) and reductions in the mortality for breast cancer. RESULTS: For both invasive cancers and DCIS, the literature indicates that prevention of in-breast invasive recurrences does not prevent death from breast cancer. Moreover, the presence of residual cancer cells in the breast after breast-conserving surgery does not compromise the cure rate. CONCLUSION: We propose an alternate (parallel) model of breast cancer wherein there is a small pool of cancer stem cells which have metastatic potential from their inception and which disseminate synchronously through several routes-to the breast stroma, to the lymph nodes and to distant organs. Cancer cells which disseminate to the breast give rise to cells which make up the bulk of the tumour mass but these are not the source of the distant metastases.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.125
GPT teacher head0.436
Teacher spread0.311 · 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 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

Citations69
Published2018
Admission routes1
Has abstractyes

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