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Record W2807817067 · doi:10.1177/2325967118777735

Arthroscopic-Assisted Latissimus Dorsi Tendon Transfer for Massive Rotator Cuff Tears: A Systematic Review

2018· review· en· W2807817067 on OpenAlexaff
Muzammil Memon, Jeffrey Kay, Emily Quick, Nicole Simunovic, Andrew Duong, Patrick Henry, Olufemi R. Ayeni

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2018
Typereview
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsSunnybrook Health Science CentreHealth Sciences CentreImpactUniversity of TorontoMcMaster University
Fundersnot available
KeywordsMedicineRotator cuffMEDLINEChecklistPhysical therapyEvidence-based medicineTearsSystematic reviewSurgeryRandomized controlled trialMeta-analysisArthroscopyAlternative medicineInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Arthroscopic-assisted latissimus dorsi tendon transfer (LDTT) has shown promising results with good outcomes in patients with massive rotator cuff tears (MRCTs), as reported by individual studies. However, to the best of the authors' knowledge, no systematic review has been performed to assess the collective outcomes of these individual studies. PURPOSE/HYPOTHESIS: The primary purpose of this study was to assess patient outcomes after arthroscopic-assisted LDTT for the management of MRCTs. The secondary objectives were to report on the management of MRCTs, including diagnostic investigations, surgical decision making, and arthroscopic techniques, as well as to evaluate the quality of evidence of the existing literature. It was hypothesized that nearly all patients were satisfied with arthroscopic-assisted LDTT and that they experienced improvements in pain symptoms, function, and strength after the procedure, with an overall complication rate of less than 10%. STUDY DESIGN: Systematic review; Level of evidence, 4. METHODS: The databases MEDLINE, Embase, and PubMed were searched from database inception (1946) until August 18, 2017, with titles, abstracts, and full-text articles screened independently by 2 reviewers. Inclusion criteria were English-language studies investigating arthroscopic-assisted LDTT for the management of MRCTs on patients of all ages. Conference papers, book chapters, review articles, and technical reports were excluded. The quality of the included studies was categorized by level of evidence and the Methodological Index for Non-Randomized Studies (MINORS) checklist. RESULTS: In total, 8 studies (7 case series [median MINORS score, 7 of 16] and 1 prospective comparative study [median MINORS score, 14 of 24]) were identified; the studies included 258 patients (258 shoulders) with MRCTs treated with LDTT using arthroscopic-assisted techniques. The decision to pursue surgery was based on both clinical findings and investigations in 5 studies, investigations only in 2 studies, and clinical findings only in 1 study. Overall, 88% of patients were satisfied with the results of surgery and experienced significant improvement in their symptoms, including shoulder pain, strength, range of motion, and overall function, over a mean follow-up period of 34.3 months. Overall, there was a low rate of complications (7%) associated with the procedure. CONCLUSION: Arthroscopic-assisted LDTT for MRCTs provides patients with marked improvement in shoulder pain, strength, and function, and the procedure is associated with a low risk of complication. Further high-quality comparative studies are warranted to validate these findings in comparison with other operative techniques.

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)0.008
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.034
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0090.007
Bibliometrics0.0100.011
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.046
GPT teacher head0.362
Teacher spread0.317 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations40
Published2018
Admission routes1
Has abstractyes

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