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Record W2035875878 · doi:10.1503/cjs.031213

Oncoplastic reduction using the vertical scar superior-medial pedicle pattern technique for immediate partial breast reconstruction

2014· article· en· W2035875878 on OpenAlexvenueno aff
Yoav Barnea, Amir Inbal, Daphna Barsuk, Tehila Menes, Arik Zaretski, David Leshem, Jerry Weiss, Schlomo Schneebaum, Eyal Gur

Bibliographic record

VenueCanadian Journal of Surgery · 2014
Typearticle
Languageen
FieldMedicine
TopicBreast Implant and Reconstruction
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOncoplastic SurgeryReduction MammoplastyBreast reductionReduction (mathematics)Breast surgeryBreast reconstructionSurgeryMammaplastyBreast cancerCancerInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Oncoplastic breast reduction in women with medium to large breasts has reportedly benefitted them both oncologically and cosmetically. We present our experience with an oncoplastic breast reduction technique using a vertical scar superior-medial pedicle pattern for immediate partial breast reconstruction. METHODS: All patients with breast tumours who underwent vertical scar superior-medial pedicle reduction pattern oncoplastic surgery at our centre between September 2006 and June 2010 were retrospectively studied. Follow-up continued from 12 months to 6 years. RESULTS: Twenty women (age 28-72 yr) were enrolled: 16 with invasive carcinoma and 4 with benign tumours. They all had tumour-free surgical margins, and no further oncological operations were required. The patients expressed a high degree of satisfaction from the surgical outcome in terms of improved quality of life and a good cosmetic result. CONCLUSION: The vertical scar superior-medial pedicle reduction pattern is a versatile oncoplastic technique that allows breast tissue rearrangement for various tumour locations. It is oncologically beneficial and is associated with high patient satisfaction.

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)0.001
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: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.951
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.029
GPT teacher head0.244
Teacher spread0.215 · 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 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

Citations32
Published2014
Admission routes1
Has abstractyes

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