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Record W4377966596 · doi:10.1097/prs.0000000000010174

A Safe and Efficient Technique for Pedicled TRAM Flap Breast Reconstruction

2023· article· en· W4377966596 on OpenAlexaff
John L. Semple, Alex Viezel-Mathieu, Sultan Al‐Shaqsi, Kathleen Armstrong

Bibliographic record

VenuePlastic & Reconstructive Surgery · 2023
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineBreast reconstructionRectus abdominis muscleSurgeryAbdominal wallPlastic surgeryBreast cancerCancer

Abstract

fetched live from OpenAlex

LEARNING OBJECTIVES: After studying this article, the participant should be able to: 1. Understand the indications for a unilateral pedicled transverse rectus abdominis (TRAM) flap-based breast reconstruction. 2. Understand the different types and designs of pedicled TRAM flap used in both immediate and delayed breast reconstruction. 3. Understand the essential landmarks and relevant anatomy of the pedicled TRAM flap. 4. Understand the steps of raising the pedicled TRAM flap, the subcutaneous transfer, and the insetting of the flap on the chest wall. 5. Understand the nature of donor-site management and closure of the defect. 6. Develop a postoperative plan for continuing care and pain management. SUMMARY: This article focuses primarily on the unilateral, ipsilateral pedicled TRAM flap. Although the bilateral pedicled TRAM flap may be a reasonable option in some cases, they have been shown to have a significant impact on abdominal wall strength and integrity. Other types of autogenous flaps using the same lower abdominal tissue, such as a free muscle-sparing TRAM or a deep inferior epigastric flap, can be performed as a bilateral procedure with less impact on the abdominal wall. Breast reconstruction with a pedicled transverse rectus abdominis flap has persisted for decades as a reliable and safe form of autologous breast reconstruction leading to a natural and stable breast shape.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.682
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.016
GPT teacher head0.252
Teacher spread0.235 · 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.

Study designOther design
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

Citations3
Published2023
Admission routes1
Has abstractyes

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