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Record W4414443676 · doi:10.1016/j.eats.2025.103894

Mobilization Techniques for Acute and Chronic Retracted Pectoralis Major Tears for Primary Repair

2025· article· en· W4414443676 on OpenAlexaff
Ali Ahmadi Pirshahid, Steven Villani, Marie‐Eve LeBel

Bibliographic record

VenueArthroscopy Techniques · 2025
Typearticle
Languageen
FieldMedicine
TopicPectus Deformity Diagnosis and Treatment
Canadian institutionsSt Joseph's Health CareWestern University
Fundersnot available
KeywordsTearsTendonConservative managementIncidence (geometry)Mobilization

Abstract

fetched live from OpenAlex

Pectoralis major (PM) tears are uncommon yet increasing in incidence in the athletic population. Previous research has favored operative management of both acute and chronic PM tears. Primary repair is indicated for both acute and chronic tears, when possible, to improve functional outcomes and cosmesis. Often, lack of viable tissue and inadequate tendon length make primary repair challenging, especially with increased chronicity and increased retraction. Autograft and allograft tendon use is described to bridge a residual gap between tendinous tissue and the footprint or to augment tissue quality. However, to our knowledge, the literature is scarce regarding detailed technical tips and tricks on how to specifically mobilize a chronically torn and/or markedly retracted PM. This article clearly describes and depicts the step-by-step approach and surgical technique for mobilizing a markedly retracted and/or chronically torn PM. It also describes and shows how to use and position an allograft patch if 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 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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.009
GPT teacher head0.333
Teacher spread0.324 · 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
Published2025
Admission routes1
Has abstractyes

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