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Record W4321787718 · doi:10.21037/abs-21-132

The impact of radiotherapy in pre-pectoral implant-based breast reconstruction: a narrative review

2023· review· en· W4321787718 on OpenAlexaboutno aff
Wafa Taher, Muhammad Nadeem, Nunzio Velotti, Maurizio Bruno Nava, Stephen F. Hamilton

Bibliographic record

VenueAnnals of Breast Surgery · 2023
Typereview
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMastectomyRadiation therapyMedicineImplantBreast reconstructionNarrative reviewCohortGeneral surgerySurgeryBreast cancerInternal medicineCancerIntensive care medicine

Abstract

fetched live from OpenAlex

Background and Objective: Pre-pectoral implant-based reconstruction (PIBR) is becoming increasingly popular. It is known that the data available so far are limited regarding the effect of post-mastectomy radiotherapy (PMRT) in PIBR. This as well true with data about effect of previous breast radiotherapy (RT). Relatively few studies were undertaken and their outcomes were released in the last few years. Some of them were designed to address impact of RT. Others just mentioned its effect on their cohort. Hence this review was undertaken to study the effect of RT (both pre- and post-mastectomy) on PIBR. Methods: A narrative review of literature was undertaken. The search strategy was limited to articles written in English language. A systematic search was performed in all electronic databases (PubMed, Web of Science, Scopus, EMBASE). All studies, over the last 10 years were included, that reported or mentioned effect of RT on patients who had PIBR. The quality of each included study was assessed with the Newcastle-Ottawa Scale (NOS). Key Content and Findings: Nineteen studies reporting on impact of RT in PIBR or mentioned its effect have met the inclusion criteria. Conclusions: PMRT appears to be well tolerated in immediate PIBR with no excess adverse effects when used with acellular dermal matrix (ADM)/mesh at least in short term. Previous RT per se is not an absolute contraindication to the procedure. More studies are needed.

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)0.003
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 score0.953
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.088
GPT teacher head0.413
Teacher spread0.325 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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