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Record W4385412628 · doi:10.1177/2325967123s00180

Poster 195: Retrospective Outcomes of Primary Rotator Cuff Repairs Involving Two or More Tendons

2023· article· en· W4385412628 on OpenAlexaff
Christian Pearsall, Sohil S. Desai, B. J. Lynch, Alan W. Reynolds, William N. Levine, David P. Trofa, Dany El-Najjar

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsColumbia College
Fundersnot available
KeywordsMedicineRotator cuffTearsSurgeryTendonDemographicsRotator cuff injuryRetrospective cohort study

Abstract

fetched live from OpenAlex

Objectives: Rotator cuff tears are one of the most common causes of shoulder dysfunction, ranging from small (0-1 cm), medium (1-3 cm), large (3-5 cm), or massive (>5 cm) in size. Alternatively, massive rotator cuff tears have also been defined by the presence of two tendon tears. The surgical management of large and massive rotator cuff tears, while also more challenging, demonstrates less favorable outcomes than that of small and medium tears. This has led to the development of various surgical repair and augmentation strategies aimed at improving post-operative outcomes and decreasing failure rates. The first objective of this study is to describe patient demographics, injury, pre- operative and intra-operative characteristics of patients undergoing surgical management of primary large and massive rotator cuff tears. The second objective is to compare post-operative changes in range of motion, re-tear rates, and re-operation rates between different repair types. Methods: Patients diagnosed and surgically managed for a multiple tendon rotator cuff tear from Jun 2019 to Jan 2021 at a tertiary academic institution were identified by retrospective chart review after IRB approval was obtained. Inclusion criteria for patients was primary operative care of a multiple tendon rotator cuff tear, which was defined as a full thickness tear of one tendon as well as a partial or full thickness tear of other tendon(s). Exclusion criteria included single tendon tears, age <18, and revision rotator cuff repairs. Demographic data collected included sex, age, smoking status, prior surgical history, and workers compensation status. Injury characteristics including duration of symptoms, tear location, side of injury (dominant vs non-dominant), mechanism of injury (traumatic vs atraumatic), steroid injections, tendon retraction, and fatty infiltration were also collected and analyzed. Operative data analyzed included repair type, suture fixation method, number of anchors placed, and concomitant procedures. Surgical management included standard repair of all torn tendons, partial repair, repair with augmentation, superior capsular reconstruction, and lower trapezius tendon transfer. Suture fixation methods included single row knotless, single row knotted, double row suture bridge, double row speed bridge, double row rip stop repair. Outcomes analyzed included change in range of motion (ROM) collected at the most recent follow-up, as well as re-operation and re-tear rates. Vertebral internal rotation values were assigned to point categories: To the side or back pocket (2 points), from the sacrum to L4 (4 points), from L3 to L1 (6 points), from T12 to T8 (8 points), and T7 and higher (10 points). Patients with < 6 months of follow up were excluded for range of motion analyses. All statistical analysis were performed using Microsoft Excel v. 16.64. Comparisons between groups were performed using either Chi-square, student t-tests, or ANOVA where appropriate. P values less than 0.05 were considered statistically significant. Results: Of the 472 cases evaluated, 140 large or massive rotator cuff repairs were identified. The mean patient age was 62.9 ± 8.9 years, and mean follow up was 8.1 ± 6.5 months. 80 patients were male, 60 were female. 35.7% of patients were current or former smokers, 12.9% had diabetes, 8.6% of patients were on worker’s compensation for the injury, 60.7% had an acute traumatic injury, and 35.7% had an atraumatic presentation. 88.6% of patients underwent a concomitant procedure. Most tear patterns involved the supraspinatus and infraspinatus (36.4%), followed by tears of the supraspinatus, infraspinatus, and subscapularis (34.3%), and lastly tears of the supraspinatus and subscapularis (29.3%). Average duration of symptoms was 13.6 ± 25.1 months. 40% of patients were treated with steroid injections prior to surgery. In decreasing order, the most common surgical procedures performed included standard repair (75%), repair with augmentation (14.3%), superior capsular reconstruction (5%), lower trapezius tendon transfer repair (2.9%), and partial repair (2.9%). Across all patients, mean flexion was 127.4° ± 46.4°pre-operatively and significantly increased to 161.9° ± 20.7° post-operatively (p<0.01). Mean ER was 42.9° ± 16.4° pre-operatively and significantly increased to 51.6° ± 12.3° post- operatively (p<0.01). IR values were also significantly higher post-operatively with 84.8% of patients reaching T12 or higher, compared to only 62.5% pre-operatively (p<0.01). Among the different repair types, no difference was observed in change of forward flexion or external rotation or in re-operation rates (p=0.71). Overall, re-tear rate was 9.3%, and re-operation rate was 4.3%. Repair type had a significant impact on change in internal rotation (p=0.03) and on re-tear rates, with partial repairs having the highest rate (50.0%), followed by repair with augmentation (15.0%), standard repair (7.6%), and both superior capsular reconstruction and lower trapezius tendon transfer (0%) (X 2 = 10.12, p=0.04). Suture fixation method had no significant impact on re-tear or re-operation rates (p=0.80, 0.72). Conclusions: An overall re-tear rate of 9.3% and re-operation of 4.3% suggests that excellent outcomes can be obtained while managing even the most difficult rotator cuff tears. Given that the highest failure rates are seen with partial repairs, this may be a patient population that can benefit from rotator cuff repair augmentation strategies which should be further examined in the future. [Table: see text][Table: see text]

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 score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.026
GPT teacher head0.321
Teacher spread0.295 · 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".

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Citations0
Published2023
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