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Record W4405289411 · doi:10.14740/wjon1935

Updates on Breast Reconstruction: Surgical Techniques, Challenges, and Future Directions

2024· review· en· W4405289411 on OpenAlexvenueno aff
Ryohei Katsuragi, Cemile Nurdan Öztürk, Kohei Chida, Gabriella Kim Mann, Arya Mariam Roy, Kenichi Hakamada, Kazuaki Takabe, Toshihiko Satake

Bibliographic record

VenueWorld Journal of Oncology · 2024
Typereview
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsBreast reconstructionMedicineBreast cancerScope (computer science)ImplantQuality of life (healthcare)SurgeryIntensive care medicineCancerInternal medicineComputer science

Abstract

fetched live from OpenAlex

The increasing global incidence of breast cancer underscores the significance of breast reconstruction in enhancing patients' quality of life. Breast reconstruction primarily falls into two categories: implant-based techniques and autologous tissue transfers. In this study, we present a comprehensive review of various aspects of implant-based reconstruction, including different types of implants, surgical techniques, and their respective advantages and disadvantages. For autologous breast reconstruction, we classified flaps and optimal harvest sites and provided detailed insights into the characteristics, benefits, and potential complications associated with each flap type. In addition, this review explores the emerging role of fat grafting, which has received increasing attention in recent years. Despite advancements, there remains substantial scope for further improvements in breast reconstruction, emphasizing not only aesthetic outcomes, but also a reduction in complications and postoperative recovery. By offering a comprehensive overview of the historical evolution, current landscape, and future prospects of breast reconstruction, this review aims to provide readers with a comprehensive understanding of breast cancer management strategies.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.036
GPT teacher head0.350
Teacher spread0.315 · 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
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

Citations7
Published2024
Admission routes1
Has abstractyes

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