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Record W3174409158 · doi:10.1177/17585732211023100

Outcomes and complications of distal humeral hemiarthroplasty for distal humeral fractures – A systematic review

2021· review· en· W3174409158 on OpenAlexaff
Ann Mary Wilfred, Shakib Akhter, Nolan S. Horner, Ahmed Aljedani, Moin Khan, Bashar Alolabi

Bibliographic record

VenueShoulder & Elbow · 2021
Typereview
Languageen
FieldMedicine
TopicElbow and Forearm Trauma Treatment
Canadian institutionsImpactMcMaster University
Fundersnot available
KeywordsMedicineSurgeryComplicationRange of motionArthroplasty

Abstract

fetched live from OpenAlex

BACKGROUND: Distal humeral hemiarthroplasty has been performed for a variety of indications with the most common being management of distal humeral fractures. This systematic review evaluates the outcomes and complications of distal humeral hemiarthroplasty for this pathology. METHODS: We searched PubMed, EMBASE, and MEDLINE for studies reporting indications and outcomes of patients undergoing distal humeral hemiarthroplasty. Study screening, risk of bias assessment, and data extraction were performed. Summery statistics were provided. RESULTS: = 163) in this review. In all studies, the indication for distal humeral hemiarthroplasty was the presence of an intraarticular, comminuted, unreconstructable fracture. The mean post-operative MEPS, FullDASH, and QuickDASH (SD) scores were 83.6 (6.1) points, 25.4 (10.3), and 15.7 (7.4) points, respectively. The mean post-operative range of motion (SD) was 106° (11°) in the flexion and extension arc and 153° (19°) in the protonation and supination arc. The overall rate of adverse events and complication was 63%. The rate for major complications was 11%. The mean total revision rate was 4% (0% to 15) and total re-operation rate was 29% (0% to 88%). CONCLUSION: Distal humeral hemiarthroplasty is a suitable option for unreconstructable distal humeral fractures and offers good functional outcomes with acceptable complication rates.

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 categoriesMeta-epidemiology (narrow)
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 score0.317
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.001
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 head0.057
GPT teacher head0.415
Teacher spread0.358 · 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.

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

Citations23
Published2021
Admission routes1
Has abstractyes

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