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Record W2802736575 · doi:10.1177/1758573218768510

Clinical outcomes and complications following primary total elbow arthroplasty using the Latitude prosthesis

2018· article· en· W2802736575 on OpenAlexaff
David J. Cinats, Aaron J. Bois, Kevin A. Hildebrand

Bibliographic record

VenueShoulder & Elbow · 2018
Typearticle
Languageen
FieldMedicine
TopicElbow and Forearm Trauma Treatment
Canadian institutionsAlberta Bone and Joint Health InstituteUniversity of Calgary
Fundersnot available
KeywordsMedicineRange of motionElbowProsthesisArthroplastyImplantSurgeryRadiographyComplication

Abstract

fetched live from OpenAlex

BACKGROUND: The Latitude total elbow arthroplasty (TEA) is an implant with limited published data on its performance and outcomes. The aim of this study was to report the short-term outcomes of the Latitude TEA as well as to describe the radiographic outcomes and complications. METHODS: The Latitude was implanted in 20 patients (23 elbows) in a linked configuration. Patients were recalled to clinic for the assessment of their range-of-motion and compared to preoperative values. Administration of functional outcome measures was also performed. RESULTS: Mean follow-up was 4.7 years (range, 1 to 7.5 years) with four elbows requiring revision. The flexion-extension arc improved from 86.6 to 101.3 (range, 76 to 126) postoperatively (p = 0.04). The average Disabilities of the Arm, Shoulder, and Hand score was 28.1 (range, 5.8 to 50.4) and the average Mayo Elbow Performance Score was 89.6 (range, 76 to 100), with 83% of elbows scoring in the good or excellent range. Radiolucencies were detected in 60% of patients and 31% of these lucencies progressed in size at the time of follow-up. CONCLUSIONS: The Latitude prosthesis provides patients with favorable clinical outcomes with improvements in their range-of-motion and a complication rate comparable to other elbow arthroplasty implants.

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 score0.018
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.068
GPT teacher head0.380
Teacher spread0.312 · 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

Citations15
Published2018
Admission routes1
Has abstractyes

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