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Record W2955648200 · doi:10.3747/co.26.4195

Position Statement on Defining and Standardizing an Oncoplastic Approach to Breast-Conserving Surgery in Canada

2019· article· en· W2955648200 on OpenAlexaffvenueabout
Angel Arnaout, Douglas C. Ross, Eman Khayat, J. C. Richardson, Marianna M Kapala, Renee Hanrahan, J. Zhang, Christopher Doherty, Muriel Brackstone

Bibliographic record

VenueCurrent Oncology · 2019
Typearticle
Languageen
FieldMedicine
TopicBreast Implant and Reconstruction
Canadian institutionsRoyal Victoria Regional Health CentreTrillium Health CentreWestern UniversityUniversity of Ottawa
Fundersnot available
KeywordsMedicineOncoplastic SurgeryBreast cancerBreast-conserving surgeryMastectomyGeneral surgeryBreast surgeryCosmesisPlastic surgerySurgeryCancerInternal medicine

Abstract

fetched live from OpenAlex

Although mastectomy is an effective procedure, it can have a negative effect on body image, sense of attractiveness, and sexuality. As opposed to the combination of breast oncologic surgery and plastic surgery, whose primary focus is on replacing lost volume, breast-conserving oncoplastic surgery (ops) redistributes remaining breast tissue in a manner that requires vision, anatomic knowledge, and an appreciation of esthetics, symmetry, and breast function. Modern surgical treatment of breast cancer can be realized only with breast and plastic surgeons working together using oncoplastic techniques to deliver superior cosmetic and cancer outcomes alike. Using this collaborative approach, oncologic and plastic surgeons in Canada have a significant opportunity to improve the care of their breast cancer patients. We propose a tri-level classification for volume displacement procedures to act as a rubric for the training of general surgeons and oncologic breast surgeons in oncoplastic breast-conserving therapy techniques. It is our position that ops enhances outcomes for many women with breast cancer and should become part of the standard repertoire of procedures used by Canadian oncologic surgeons treating breast cancer.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.521
Threshold uncertainty score0.967

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 head0.051
GPT teacher head0.326
Teacher spread0.276 · 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

Citations21
Published2019
Admission routes3
Has abstractyes

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