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Record W2751610467 · doi:10.1177/1758573217728292

The effect of implant linking and ligament integrity on humeral loading of a convertible total elbow arthroplasty

2017· article· en· W2751610467 on OpenAlexaff
James R. Brownhill, J. Whitcomb Pollock, Louis M. Ferreira, James A. Johnson, Graham JW King

Bibliographic record

VenueShoulder & Elbow · 2017
Typearticle
Languageen
FieldMedicine
TopicElbow and Forearm Trauma Treatment
Canadian institutionsSt Joseph's Health CareWestern University
Fundersnot available
KeywordsElbowMedicineImplantValgusLigamentMedial collateral ligamentArthroplastyStructural integrityOrthodonticsAnatomySurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Both unlinked and linked total elbow arthroplasty (TEA) implants have been employed with no consensus as to the optimal design. The present study aimed to evaluate the effect of collateral ligament integrity and implant linkage on wear-inducing loads in a convertible TEA. METHODS: Eight fresh frozen upper extremities were tested in an elbow motion simulator. A convertible TEA with an instrumented humeral stem was inserted using computer navigation. Elbow kinematics and humeral loading were recorded with the TEA both linked and unlinked. The collateral ligaments were then sectioned and testing was repeated. RESULTS: In the dependent position, there was no effect of implant linkage or ligament sectioning on humeral loading. Humeral loading was significantly greater following sectioning of the collateral ligaments but not after linking the TEA with the arm in the valgus position. Humeral loading was significantly greater after linking the TEA but not after sectioning of the collateral ligaments and with the arm in the varus position. CONCLUSIONS: Collateral ligament integrity reduces wear-inducing loads for both an unlinked and linked TEA. Linkage of a convertible TEA increases humeral loading, which may have detrimental effects on implant longevity.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.543
Threshold uncertainty score0.515

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 head0.021
GPT teacher head0.315
Teacher spread0.294 · 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".

Quick stats

Citations12
Published2017
Admission routes1
Has abstractyes

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