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Record W4415438850 · doi:10.1302/1358-992x.2025.10.101

RADIAL HEAD ARTHROPLASTY FOR RADIAL HEAD FRACTURE IS ASSOCIATED WITH GREATER SURVIVORSHIP THAN OPEN REDUCTION INTERNAL FIXATION: POPULATION-BASED COHORT STUDY

2025· article· en· W4415438850 on OpenAlexaffabout
Ujash Sheth, Ning Chang, Priscila Pequeno, Michael J. Paterson, Diane Nam

Bibliographic record

VenueOrthopaedic Proceedings · 2025
Typearticle
Languageen
FieldMedicine
TopicElbow and Forearm Trauma Treatment
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsInternal fixationRadial head fractureSurvivorship curveRetrospective cohort studyArthroplastyCohort studyReduction (mathematics)Orthopedic surgery

Abstract

fetched live from OpenAlex

Radial head fractures account for one-third of all fractures around the elbow. Despite this, there remains significant debate with respect to the optimal treatment of unstable radial head fractures with significant articular displacement, comminution, or mechanical block to motion. Radial head arthroplasty (RHA) and open reduction and internal fixation (ORIF) are the current surgical treatments employed in the management of these fractures. Systematic reviews comparing RHA to ORIF have noted greater cost-effectiveness, fewer reoperations, lower complication rates and higher patient satisfaction among patients undergoing RHA. However, these findings are largely based on small retrospective data. Moreover, concerns exist regarding the longevity of the radial head implant, especially among young patients who undergo RHA. A population-based cohort study was performed utilizing administrative healthcare data from Ontario, Canada. All adults, 16 years of age and older in Canada's most populous province, who sustained a radial head fracture between April 1, 2002 and March 31, 2020 were identified. Patients undergoing ORIF, RHA or radial head excision for a radial head fracture were followed for subsequent reoperation. An unadjusted competing risk survival analysis was performed to tally the cumulative incidence of all-cause revision procedures. Logistic regression models were constructed to identify the patient-, provider- and fracture-related factors associated with reoperation following ORIF and RHA. A total of 3159 patients underwent surgical management of a radial head fracture during the study period. The majority of patients were female (55.9%). The mean age of patients who underwent ORIF, RHA and excision was, 43.9, 51.1, 52.6 years, respectively. The rate of reoperation in the ORIF, RHA and excision cohort was 7.7%, 3.8% and 0.8%. A statistically significant difference in cumulative incidence function (i.e., survivorship) was noted favoring RHA over ORIF (P < 0.0001) (Figure 1). The most frequently performed secondary procedure was removal of hardware. Other secondary procedures included, total elbow arthroplasty, elbow arthroscopy, radial head replacement, irrigation & debridement of the radius, capsular release, radial head excision, excision of heterotopic ossification and loose body removal. The odds of reoperation among those undergoing ORIF increased with a greater comorbidity score (adjusted OR 1.47; 95% CI, 1.04–2.07, P = 0.03) and surgery performed at a teaching hospital (adjusted OR 1.76; 95% CI, 1.22–2.53, P = 0.0023). The odds of reoperation among those who had a RHA increased when surgery was performed overnight (between midnight and 7AM) (adjusted OR 3.27; 95% CI, 1.10–9.70, P = 0.03) and decreased when RHA was performed in combination with a ligament repair (adjusted OR 0.62; 95% CI, 0.42–0.94, P = 0.02). Radial head fractures treated with radial head arthroplasty have a significantly lower rate of reoperation and greater survivorship. Concomitant ligament repair at the time of radial head arthroplasty may be a protective factor, while performing a radial head arthroplasty overnight (between midnight and 7AM) may increase the odds of reoperation. Prospective studies are needed to further elucidate the key predictors for long-term survivorship of RHA, especially among young patients. For any figures or tables, please contact the authors directly.

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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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.025
GPT teacher head0.301
Teacher spread0.277 · 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 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

Citations0
Published2025
Admission routes2
Has abstractyes

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