Impact of Patient Demographics, Surgeon Volume, and Hospital Type on Distal Radius Fracture Surgery Outcomes: A Population-Based Study
Bibliographic record
Abstract
Abstract This study aimed to evaluate the impact of patient demographics, surgeon volume, and hospital type on composite outcomes, infection, and revision following surgery for acute, isolated distal radius fractures (DRFs). This population-based study examined Ontario administrative health data from 2010 to 2020, identifying 13,389 adults who underwent surgical fixation for acute, isolated DRFs. Patients with open fractures, other associated injuries, neurovascular injuries, prior surgery on the same limb, or any other factors that could worsen prognosis were excluded. Covariates included were time to surgery, patient demographics (age, biological sex, comorbidities, rural residence, income quintile), surgeon factors (volume, fixation type), fracture type (intra-articular vs. extra-articular), and hospital type (teaching vs. non-teaching). The primary outcome was a composite measure of complications, including infection, revision surgery, and hardware removal. Secondary outcomes included postoperative infection and revision procedures. Time-to-event Cox proportional multivariable models were applied to estimate hazard ratios (HRs) with 95% confidence intervals (CIs), adjusting for covariates. A total of 13,389 patients were included in the analysis. Higher surgeon volume was associated with improved outcomes: Every additional five DRF surgeries performed in the prior year reduced the risk of composite complications by 4% (HR 0.96, 95% CI 0.94–0.98; p < 0.001) and the risk of revision surgery by 10% (HR 0.90, 95% CI 0.86–0.93; p < 0.001). Rural residence was associated with a 44% higher risk of postoperative infection (HR 1.43, 95% CI 1.08–1.89; p = 0.01). Increased comorbidity burden, measured by the Johns Hopkins score, was consistently associated with worse outcomes: Each one-point increase corresponded to a 6.4% higher likelihood of composite complications (HR 1.06, 95% CI 1.05–1.08; p < 0.001). Female sex was protective across outcomes, reducing the risk of infection by 25% (HR 0.75, 95% CI 0.59–0.96; p = 0.02) and revision surgery by 19% (HR 0.81, 95% CI 0.68–0.98; p = 0.02). Older age was associated with a modest but consistent protective effect, reducing the risk of composite complications by 1% per year of age (HR 0.99, 95% CI 0.98–0.99; p < 0.001). Surgeon volume independently reduced complications and revisions after DRF surgery, while rural residence and higher comorbidity burden markedly increased infection risk. The protective effects of female sex and older age highlight the importance of nuanced risk assessment. Strategies to expand access to high-volume surgeons and targeted perioperative care may improve outcomes and reduce disparities in fracture management. Level III.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".