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Record W4411394431 · doi:10.1016/j.eats.2025.103699

Bridging Reconstruction Using a Biocomposite Scaffold for Large/Massive Rotator Cuff Tears: A Technique Guide

2025· article· en· W4411394431 on OpenAlexaff
Samora Maranya, Sarah Remedios, Helen Crofts, Ivan Wong

Bibliographic record

VenueArthroscopy Techniques · 2025
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsUniversity of British ColumbiaNova Scotia HospitalNova Scotia Health AuthorityDalhousie University
FundersDePuy Synthes SpineConMedSmith and Nephew
KeywordsRotator cuffMedicineBiocompositeTearsBridging (networking)ScaffoldSurgeryBiomedical engineeringComposite materialComputer scienceMaterials science

Abstract

fetched live from OpenAlex

Given the proposed mechanism of failure in maximal repair at the suture-tendon interface and the inherent inferior quality of cuff tissue in large and massive rotator cuff tears, a solution providing both strength and improved cuff biology remains the main goal in preventing retears and allowing enhanced rehabilitation to restore cuff muscle strength. This has led to graft-based approaches for reconstruction in shoulder repair, including bridging reconstruction. Biocomposite grafts encourage new tissue formation but lack structural support, while human dermal allografts provide structural support but fall short in tendon integration. Thus, a biocomposite scaffold is introduced to combat these limitations. In this technique, we describe placing the BioBrace graft (Biorez), eliminating tension at the graft-suture interface, providing strength and tissue integration.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.018
GPT teacher head0.359
Teacher spread0.342 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations1
Published2025
Admission routes1
Has abstractyes

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