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Record W4415438798 · doi:10.1302/1358-992x.2025.10.144

LOWER TRAPEZIUS TENDON TRANSFER FOR MASSIVE IRREPARABLE ROTATOR CUFF TEARS IMPROVES OUTCOMES IN PATIENTS WITH HIGH-GRADE FATTY INFILTRATION OF TERES MINOR

2025· article· en· W4415438798 on OpenAlexaff
Eva M. Gusnowski, Panayiotis D. Megaloikonomos, Sheila McRae, Eric J. Wagner, P.B. MacDonald, Jarret M. Woodmass

Bibliographic record

VenueOrthopaedic Proceedings · 2025
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsSaint John Regional Hospital
Fundersnot available
KeywordsRotator cuffTearsTendonTendon transferInternal rotationCuffSubacromial impingement

Abstract

fetched live from OpenAlex

Surgical management of massive irreparable rotator cuff tears has increasingly used salvage procedures such as tendon transfers to preserve glenohumeral articulation. Appropriate patient selection is crucial, yet there is a paucity of evidence investigating factors leading to improved outcomes. Success of latissimus dorsi tendon transfer (LDTT) has been linked to low levels of fatty infiltration of the teres minor (TM) muscle. However, the effect of TM fatty infiltration has not been studied in newer techniques such as lower trapezius tendon transfers (LTTT). This study aims to correlate post-operative outcomes of LTTT in massive irreparable rotator cuff tears to the degree of TM fatty infiltration. In this prospective longitudinal observational study, patients with massive rotator cuff tears undergoing arthroscopic-assisted LTTT by a single surgeon were screened. For all consented patients, TM fatty infiltration on pre-operative MRI was graded using the Goutallier classification by two orthopaedic surgeons not involved in the surgery. Two groups were created based on TM fatty infiltration: Group A included Grades 0 and 1 (no or little fatty infiltration) and Group B included Grades 2 to 4 (moderate to severe fatty infiltration). Participants completed the SANE score and satisfaction questionnaire at pre-, 12- and/or 24-months post-operative along with a clinical assessment. Between group comparisons were performed using independent t-tests assuming unequal variance and pre- vs post-operative comparisons were performed for each group using paired t-tests. Rate of external rotation (ER) lag sign was compared between groups using Fisher's exact test. Significance was defined as p < 0.05. Twenty-six patients met inclusion criteria, with 18 patients in Group A and 8 in Group B. There were no differences between groups with respect to pre-operative SANE score, degrees of active forward elevation or degrees of active ER. Eight of 18 patients (44.4 %) in Group A had an ER lag sign, compared to 5 of 8 patients (62.5%) in Group B, but this was not statistically significant (p=0.672). Significant post-operative improvements in SANE score were found in both groups (Figure 1) with no differences between groups. Pre-operative ER strength of the affected arm was significantly different in Group A (3.9kg) versus (Group B (0.58kg; p=0.011; Figure 1). However, ER strength was similar post-operatively (p=0.587; Figure 2). 0.05. In our study patients with any degree of TM fatty infiltration benefited from LTTT and experienced improvement in outcomes. Patients with moderate to severe TM fatty infiltration (Group B) had lower baseline ER strength, but this difference was no longer evident post-operatively. Although overall patient numbers in this study are small given the rarity of this condition and procedure, these outcomes suggest LTTT provides significant benefit to patients independent of TM fatty infiltration. This is in contrast to the LDTT, which is less successful in patients with moderate to severe TM fatty infiltration. Therefore, LTTT is a suitable salvage procedure for any degree of TM fatty infiltration, and should be strongly considered as an alternate procedure to LDTT in patients with high grade TM fatty infiltration. For any figures or tables, please contact the authors directly.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.083
Threshold uncertainty score0.790

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.009
GPT teacher head0.258
Teacher spread0.250 · 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.

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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