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Record W4415430230 · doi:10.1302/1358-992x.2025.10.027

PATIENT AND SURGICAL RISK FACTORS FOR SURGICAL SITE INFECTION IN LOWER EXTREMITY ONCOLOGIC ENDOPROSTHETIC RECONSTRUCTION: A SECONDARY ANALYSIS OF THE PARITY TRIAL DATA

2025· article· en· W4415430230 on OpenAlexaff
David Slawaska‐Eng, Michelle Ghert, Aaron Gazendam, Patrícia Schneider, Joseph K. Kendal, Ricardo Gehrke Becker, Nicholas M. Bernthal, Maria‐João Paulo, Franciele de Freitas

Bibliographic record

VenueOrthopaedic Proceedings · 2025
Typearticle
Languageen
FieldMedicine
TopicSurgical site infection prevention
Canadian institutionsHamilton Health Sciences
Fundersnot available
KeywordsUnivariate analysisMultivariate analysisAntibiotic prophylaxisProportional hazards modelSoft tissueRandomized controlled trialOrthopedic surgerySurgical site infectionBone cementSoft tissue sarcoma

Abstract

fetched live from OpenAlex

The specific risk factors for surgical site infection (SSI) in orthopaedic oncology patients undergoing endoprosthetic reconstruction have not previously been evaluated in a large prospective cohort. The current study aims to define patient and procedure-specific risk factors for SSI in patients undergoing surgical excision and endoprosthetic reconstruction of the lower extremity for oncologic indications using the prospectively collected data of the Prophylactic Antibiotic Regimens in Tumor Surgery (PARITY) trial. PARITY was a multicenter, blinded, parallel two-arm design randomized controlled trial that aimed to determine the effect of long (5 days) vs. short duration (24 hours) postoperative prophylactic antibiotics on the rate of SSI in patients undergoing surgical excision of the femur or tibia. The primary analysis of the PARITY study of 604 eligible patients was published in the Journal of the American Medical Association (Oncology) on January 6, 2022. In this secondary analysis of the PARITY data, a multivariate Cox proportional hazards regression model was constructed to explore predictors of SSI within one year postoperatively. Based on the outcomes of the univariate analysis and theoretical relationships, the following variables were selected for inclusion in the regression model: age, sex, tumor location (femur vs. tibia) and type (primary bone vs. soft tissue sarcoma invading bone vs. oligometastatic bone disease), soft tissue mass, preoperative neutropenia, neoadjuvant chemotherapy, operative time, total muscle excised, intraoperative vancomycin powder use, silver coated prosthesis, prosthesis betadine soak, arthroplasty helmet use, operative laminar flow, postoperative suction drain, urinary catheter, postoperative negative pressure wound therapy, hospital length of stay (LOS) and adjuvant chemotherapy. The results of the model are presented with hazards ratios (HR) and 95% confidence intervals (CI). A total of 96 of 604 patients (15.9%) experienced an SSI. Of the 22 variables analysed in the univariate analysis, four variables achieved statistical significance: tumor type, operative time, volume of muscle excised and hospital LOS. However, only hospital LOS was found to be independently predictive of SSI in the multivariate regression analysis (HR = 1.03, 95% CI = [1.01–1.05], P = 0.001). An omnibus test of model coefficients demonstrated that the model showed significant improvement over the null model (χ2 = 76.6, P 0.7 as a cut off for exclusion. This secondary analysis of the PARITY study data found that among the potential risk factors for SSI following endoprosthetic reconstruction of the lower extremity, the only independent risk factor on multivariate analysis was hospital LOS. It therefore may be reasonable for clinicians to consider streamlined discharge plans for orthopaedic oncology patients to potentially reduce the risk for SSI.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.052
Threshold uncertainty score0.548

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.295
Teacher spread0.273 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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