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Record W4385412745 · doi:10.1177/2325967123s00074

Paper 49: Bridging Reconstruction for Large-to-Massive Rotator Cuff Tears Has a Low Rate of Cuff Arthropathy Progression at A Minimum Five-Year Follow-Up

2023· article· en· W4385412745 on OpenAlexaffabout
Kazuha Kizaki, Sara Sparavalo, Jie Ma, Ivan Wong, Dip Sports Med

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineRotator cuffTearsSurgeryCuffArthroplastyArthropathyRotator cuff injuryOsteoarthritis

Abstract

fetched live from OpenAlex

Objectives: Rotator cuff tears cause pain, muscle weakness, and difficulty with overhead activity. While smaller tears are easier to repair, large-to-massive cuff tears (>3 cm in size) are considered irreparable. For patients with large-to-massive cuff tears, surgical options include maximal repair, superior capsular reconstruction, bridging reconstruction, tendon transfers and reverse total shoulder arthroplasty (rTSA). Bridging reconstruction was developed to improve outcomes and to avoid the morbidity associated with other technique. Bridging reconstruction, also known as graft interposition, has been shown to have superior outcomes as compared to maximal repair with better patient-reported outcomes at two-years post-operatively as recently demonstrated in a randomized controlled trial. These positive results are maintained at five years, however the midterm changes in progression of rotator cuff arthropathy and conversion to rTSA have not been assessed. The purpose is to assess the progression of rotator cuff arthropathy in bridging reconstruction patients at a five-year follow-up. Methods: Forty-four patients who underwent bridging reconstruction between 2012 and 2017 for large-to-massive rotator cuff tears were included. All patients had a minimum five-year follow-up. Data collected included demographics, pre- and post-operative Western Ontario Rotator Cuff (WORC) Index, conversion to rTSA, and X-ray readings (including acromiohumeral distance (as measured on anterior-posterior pre/post-operative radiographs) and Hamada grades for rotator cuff arthropathy). Furthermore, a sub-group analysis was performed on post-operative MRIs for graft status. Results: The mean age at surgery was 59.9± 10.8 years with a mean follow-up of 7.3±1.4 years. The population was mostly male (70.5%). Pre-operatively, seven patients had mild rotator cuff arthropathy with Hamada grade 2-3, and one patient had Hamada grade 4B. The rest of the patients had Hamada grade one preoperatively. At a minimum five-year postoperative follow-up, only one patient had a rTSA, resulting in a survivorship rate of 98%. Two patients (4.6%) had post-operative Hamada grade 4. One patient had progression of cuff arthropathy from pre-operative Hamada 3 to post-operative Hamada 4A. The other patient maintained Hamada grade 4B from pre-operative to post-operative. Patients with progression of cuff arthropathy (i.e. higher post-operative Hamada grade) appear to have a higher possibility of complete post-operative graft tears. [SS1] [JM2] There were no correlations between progression of cuff arthropathy and WORC score at five years. Conclusions: At a minimum 5-year with a mean follow-up of 7.3 years, bridging reconstruction showed 98% survivorship rate with a low rate of conversion to rTSA and a low progression of cuff arthropathy with only 4.6% of patients having advanced RCA with Hamada grade 4.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.298
Teacher spread0.280 · 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 designObservational
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 routes2
Has abstractyes

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