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Record W4323980575 · doi:10.47191/ijmscrs/v3-i3-25

Correcting the Tuberous Breast Deformity by the Moufarrege Total Posterior Pedicle: An Architectural Reconstruction

2023· article· en· W4323980575 on OpenAlexaff
Richard Moufarrège, Ramy Schoucair, Marion Aribert, Cyril Awaida, Romain Laurent, Georgio Gholam

Bibliographic record

VenueInternational Journal of Medical Science and Clinical Research Studies · 2023
Typearticle
Languageen
FieldMedicine
TopicBreast Implant and Reconstruction
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineDeformityBreast reductionAreolaSurgeryReduction (mathematics)Breast surgeryPlastic surgeryBreast cancerInternal medicine

Abstract

fetched live from OpenAlex

Tuberous breast deformity is a common congenital breast anomaly that remains one of the most challenging deformities to correct in plastic surgery. It is characterized by a large areola with a cupola deformity, a short breast lower segment, a “nosing down” of the breast, and a narrowness of the breast implantation base. The classification and surgical treatment of this pathological condition have varied extensively. The Total Posterior Pedicle breast reduction technique, described by Richard Moufarrege in 1982, is an effective corrective surgery for treating this often insightful and rebellious deformity. It consists of dissecting the skin away from the breast tissue offering free access to all breast quadrants. This technique is known for its robust blood supply to the nipple areolar complex, the preservation of the nipple areolar complex sensation, and for the conservation of the breastfeeding function. In this article, we also elucidate the reasons why the Moufarrege Total Posterior Pedicle breast reduction technique corrects all the vicious details inherent to the tuberous breast.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.158
GPT teacher head0.527
Teacher spread0.368 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2023
Admission routes1
Has abstractyes

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