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Record W2739717841 · doi:10.1007/s11999-017-5413-7

Does Humeral Component Lateralization in Reverse Shoulder Arthroplasty Affect Rotator Cuff Torque? Evaluation in a Cadaver Model

2017· article· en· W2739717841 on OpenAlexaff
Kevin Chan, G. Daniel G. Langohr, Matthew D. Mahaffy, James A. Johnson, George S. Athwal

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2017
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsSt Joseph's Health CareWestern University
Fundersnot available
KeywordsMedicineRotator cuffShouldersHumerusArthroplastyCadaveric spasmBiomechanicsCadaverLateralization of brain functionOrthodonticsAnatomySurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Humeral component lateralization in reverse total shoulder arthroplasty (RTSA) may improve the biomechanical advantage of the rotator cuff, which could improve the torque generated by the rotator cuff and increase internal and external rotation of the shoulder. PURPOSE: The purpose of this in vitro biomechanical study was to evaluate the effect of humeral component lateralization (or lateral offset) on the torque of the anterior and posterior rotator cuff. METHODS: Eight fresh-frozen cadaveric shoulders from eight separate donors (74 ± 8 years; six males, two females) were tested using an in vitro simulator. All shoulders were prescreened for soft tissue deficit and/or deformity before testing. A custom RTSA prosthesis was implanted that allowed five levels of humeral component lateralization (15, 20, 25, 30, 35 mm), which avoided restrictions imposed by commercially available designs. The torques exerted by the anterior and posterior rotator cuff were measured three times and then averaged for varying humeral lateralization, abduction angle (0°, 45°, 90°), and internal and external rotation (-60°, -30°, 0°, 30°, 60°). A three-way repeated measures ANOVA (abduction angle, humeral lateralization, internal rotation and external rotation angles) with a significance level of α = 0.05 was used for statistical analysis. RESULTS: Humeral lateralization only affected posterior rotator cuff torque at 0° abduction, where increasing humeral lateralization from 15 to 35 mm at 60° internal rotation decreased external rotation torque by 1.6 ± 0.4 Nm (95% CI, -0.07 -1.56 Nm; p = 0.06) from 4.0 ± 0.3 Nm to 2.4 ± 0.6 Nm, respectively, but at 60° external rotation increased external rotation torque by 2.2 ± 0.5 Nm (95% CI, -4.2 to -0.2 Nm; p = 0.029) from 6.2 ± 0.5 Nm to 8.3 ± 0.5 Nm, respectively. Anterior cuff torque was affected by humeral lateralization in more arm positions than the posterior cuff, where increasing humeral lateralization from 15 to 35 mm when at 60° internal rotation increased internal rotation torque at 0°, 45°, and 90° abduction by 3.2 ± 0.5 Nm (95% CI, 1.1-5.2 Nm; p = 0.004) from 6.6 ± 0.6 Nm to 9.7 ± 0.6 Nm, 4.0 ± 0.3 Nm (95% CI, 2.8-5.0 Nm; p < 0.001) from 1.7 ± 1.0 Nm to 5.6 ± 0.9 Nm, and 2.2 ± 0.2 Nm (95% CI, 1.4-2.9 Nm; p < 0.001) from 0.6 ± 0.6 Nm to 2.8 ± 0.6 Nm, respectively. In neutral internal and external rotation, increasing humeral lateral offset from 15 to 35 mm increased the internal rotation torque at 45˚ and 90˚ abduction by 1.5 ± 0.3 Nm (95% CI, 0.2-2.7 Nm; p = 0.02) and 1.3 ± 0.2 Nm (95% CI, 0.4-2.3 Nm; p < 0.001), respectively. CONCLUSIONS: Humeral component lateralization improves rotator cuff torque. CLINICAL RELEVANCE: The results of this preliminary in vitro cadaveric study suggest that the lateral offset of the RTSA humeral component plays an important role in the torque generated by the anterior and posterior rotator cuff. However, further studies are needed before clinical application of these results. Increasing humeral offset may have adverse effects, such as the increased risk of implant modularity, increasing tension of the cuff and soft tissues, increased costs often associated with design modifications, and other possible as yet unforeseen negative consequences.

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)0.006
metaresearch head score (Gemma)0.001
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 score0.227
Threshold uncertainty score0.584

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.162
GPT teacher head0.495
Teacher spread0.334 · 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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Citations56
Published2017
Admission routes1
Has abstractyes

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