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Record W4414645432 · doi:10.1097/bot.0000000000003086

Impact of Time-to-Surgery on Adverse Outcomes for Distal Radius Fractures: A Population-Based Study

2025· article· en· W4414645432 on OpenAlexaffabout
Jonathan Persitz, Heather L. Baltzer, Andrew Calzavara, Jesse Wolfstadt, Ryan Paul, Andrea Chan, Samantha Lee, Brandon Zagorski, David R. Urbach

Bibliographic record

VenueJournal of Orthopaedic Trauma · 2025
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Surgery and Rehabilitation
Canadian institutionsWomen's College HospitalHospital for Sick ChildrenInstitute for Clinical Evaluative SciencesSinai Health SystemToronto Western HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsRADIUSAdverse effectDistal radius fractureMEDLINE

Abstract

fetched live from OpenAlex

OBJECTIVES: To determine the optimal time window for surgical fixation of acute, isolated distal radius fractures (DRFs) in order to minimize postoperative complications. METHODS: Design: Retrospective, population-based cohort study. SETTING: Province-wide analysis using administrative health databases in Ontario, Canada. PATIENT SELECTION CRITERIA: Adult patients (≥18 years) who underwent surgical fixation for acute, isolated DRFs (OTA/AO 2R3) between 2010 and 2020 were included. Patients with open fractures, polytrauma, neurovascular injury, or fractures marked as urgent were excluded. Wait time to surgery was defined as days from emergency department presentation to operative intervention. OUTCOME MEASURES AND COMPARISONS: The primary outcome was a composite of complications including any complication or revision surgery within 10 years. Secondary outcomes included infection and revision individually. Wait time to surgery was analyzed both as a continuous variable and as a categorical variable. For the categorical analysis, patients were stratified into seven intervals (0-2, 3-5, 6-9, 10-15, 16-20, 21-25, and 26-30 days). These cut-offs were chosen to provide relatively small, evenly distributed time ranges while maintaining sufficient patient numbers within each group to ensure statistical power and model stability. This approach allowed for meaningful comparisons across the surgical wait-time spectrum while complementing the continuous analysis. Cox multivariable models were used to estimate hazard ratios (HRs), adjusting for demographics, comorbidities, fracture and fixation type, surgeon volume, and hospital type. An instrumental variable analysis using institutional wait times was performed to address confounding by indication. RESULTS: A total of 13,389 patients met inclusion criteria. Patients were predominantly female (71.2%) with a mean age of 55.7 years (Range 18-95). The 0-2 day group served as the reference and demonstrated the highest complication rates. Compared with this group, patients treated within 6-20 days had a significantly lower risk of composite complications, with the greatest benefit observed in the 6-9 day (HR 0.84, 95% CI: 0.73-0.97, P=0.02) and 10-15 day (HR 0.78, 95% CI: 0.67-0.90, P=0.001) subgroups. Infection risk was similarly lowest in the 6-15 day window, with the most favorable outcomes in the 10-15 day subgroup (HR 0.59, 95% CI: 0.41-0.84, P=0.003). Institutional-level analysis showed a 30% lower infection risk for treatment within 6-15 days compared to 1-5 days (HR 0.70, 95% CI: 0.56-0.87, P=0.002). Surgeries delayed >25 days showed a non-significant trend toward worse outcomes (HR 1.10, 95% CI: 0.75-1.32, P=0.88). CONCLUSIONS: Surgical fixation of distal radius fractures within 6-15 days was associated with the lowest observed rates of composite complications and infection. These findings suggest that this timeframe may represent an optimal window for intervention. By evaluating multiple discrete time points, this study contributes to the understanding of "when to operate," complementing prior literature focused primarily on delayed surgery. LEVEL OF EVIDENCE: Level III.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.105
Threshold uncertainty score0.210

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.337
Teacher spread0.322 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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