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Record W4415430076 · doi:10.1302/1358-992x.2025.10.091

COST COMPARISON BETWEEN OPEN REDUCTION AND INTERNAL FIXATION AND FUNCTIONAL BRACING FOR HUMERAL DIAPHYSEAL FRACTURE MANAGEMENT

2025· article· en· W4415430076 on OpenAlexaffabout
Prism Schneider, Ahmed Negm, S. Yee, Kenneth P Goldstein, M. Amedeo, Rudolph Reindl, Gregory K. Berry, Herman Johal

Bibliographic record

VenueOrthopaedic Proceedings · 2025
Typearticle
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsInternal fixationNonunionIntramedullary rodRandomized controlled trialBracingDiaphysisReduction (mathematics)HumerusExternal fixation

Abstract

fetched live from OpenAlex

Fractures of the humeral diaphysis have traditionally been treated non-operatively using functional bracing; however, the recent randomized controlled trial (RCT) performed by the Canadian Orthopaedic Trauma Society has demonstrated a symptomatic nonunion rate of over 15%. Coaptation splinting is reported to have lower direct costs compared to functional bracing for non-operative management. Prior studies have found increased direct surgical costs for intramedullary nail fixation when compared with open reduction and internal fixation (ORIF) for humeral diaphyseal fractures. This study aimed to complete a cost analysis comparing the differences in direct and in-directs costs between ORIF and functional bracing relative to the improvement in SMFA functional outcome scores for isolated humeral diaphyseal fractures. This is a prespecified secondary analysis from a large, multi-centre RCT comparing ORIF (plate and screws) with non-operative treatment (functional bracing) for humeral diaphyseal fractures. Patients were included if they were 18 years or older with displaced humeral diaphyseal fracture (AO/OTA 12-A, B, C) amenable to both treatments and presentation within 21 days from injury. Patients were excluded if there was an open fracture, multiple injuries, or nerve injury requiring repair. A cost-analysis was completed using data on costs and outcomes from the trial, including both directs costs of care (implant costs, hospital stay, readmissions, complications) as well as indirect costs (time off of work). Change in Selective Functional Movement Assessment (SMFA) scores were used to quantify effectiveness, with a minimal clinical important difference of 7.3 used as a threshold for clinically significant improvement. Costs and outcomes were captured over a 1-year period. A monte carlo model was utilized to generate incremental cost-effectiveness ratios (ICER), using a probabilistic sampling strategy. A total of 168 patients from 12 participating sites were included in the final analysis (84 in ORIF group and 84 in the non-operative group). There was no significant baseline demographic differences between groups and 1-year follow-up rate was 85%. Overall change improvement in SMFA score for the ORIF group was 3.32 points, while the overall change for the non-operative group was 2.88 points (Table 1). When considering direct health care costs alone, the average costs for the ORIF group were $4,216.62, while the average costs for the non?operative group were $1,068.81 (Table 1). This resulted in an incremental cost difference of $3,151, and an ICER of $7,002 per point improvement in SMFA, or $51,116 to obtain clinical improvement in SMFA functional outcome score. When additional indirect costs are considered, the average total costs for the ORIF group were $26,692.40, while the average total costs for the non-operative group were $34, 806.21 (Table 1). This resulted in an incremental societal cost difference of $8,112 in favor of the ORIF group as the dominant intervention. Data from a large, multi-centre RCT supports that ORIF of humeral diaphyseal fractures would be considered a cost-effective treatment option when considering direct health care related costs and is a dominant intervention when indirect costs such as time off work and surgical intervention for fracture nonunion are considered. Providing value-based care has become increasingly important, and using traditional thresholds in combination with shared decision making with patients and stakeholders based on functional goals. For any figures or tables, please contact the authors directly.

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 score0.178
Threshold uncertainty score0.517

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 head0.032
GPT teacher head0.332
Teacher spread0.300 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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