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Record W4384665250 · doi:10.2106/jbjs.22.00815

Risk Factors for All-Cause Early Reoperation Following Tumor Resection and Endoprosthetic Reconstruction

2023· article· en· W4384665250 on OpenAlexaff
Joseph K. Kendal, David Slawaska‐Eng, Aaron Gazendam, Patricia Schneider, Lauren E. Wessel, Michelle Ghert, Nicholas M. Bernthal

Bibliographic record

VenueJournal of Bone and Joint Surgery · 2023
Typearticle
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineSurgeryUnivariate analysisProportional hazards modelHazard ratioSoft tissueMalignancyProspective cohort studyPopulationMultivariate analysisInternal medicineConfidence interval

Abstract

fetched live from OpenAlex

BACKGROUND: Oncologic resection and endoprosthetic reconstruction of lower-extremity musculoskeletal tumors are complex procedures fraught with multiple modes of failure. A robust assessment of factors contributing to early reoperation in this population has not been performed in a large prospective cohort. The aim of the present study was to assess risk factors for early reoperation in patients who underwent tumor excision and endoprosthetic reconstruction, with use of data from the Prophylactic Antibiotic Regimens in Tumor Surgery (PARITY) trial. METHODS: Baseline characteristics were assessed, including age, sex, tumor type, tumor location, presence of a soft-tissue mass, diabetes, smoking status, chemotherapy use, and neutropenia. Operative factors were recorded, including operative time, topical antibiotics, silver-coated prosthetics, endoprosthetic fixation, extra-articular resection, length of bone resected, margins, tranexamic acid, postoperative antibiotics, negative-pressure wound therapy, and length of stay. Univariate analysis was utilized to explore the differences between patients who did and did not undergo reoperation within 1 year postoperatively, and a multivariate Cox proportional hazards regression model was utilized to explore the predictors of reoperation within 1 year. RESULTS: A total of 155 (25.7%) of 604 patients underwent ≥1 reoperation. In univariate analysis, tumor type (p < 0.001), presence of a soft-tissue mass (p = 0.045), operative time (p < 0.001), use of negative-pressure wound therapy (p = 0.010), and hospital length of stay (p < 0.001) were all significantly associated with reoperation. On multivariate assessment, tumor type (benign aggressive bone tumor versus primary bone malignancy; hazard ratio [HR], 0.15; 95% confidence interval [CI], 0.04 to 0.63; p = 0.01), operative time (HR per hour, 1.15; 95% CI, 1.10 to 1.23; p < 0.001), and use of negative-pressure wound therapy (HR, 1.93; 95% CI, 1.30 to 2.90; p = 0.002) remained significant predictors of reoperation within 1 year. CONCLUSIONS: Independent variables associated with reoperation within 1 year in patients who underwent tumor resection and endoprosthetic reconstruction included tumor type (benign aggressive bone tumor versus primary bone malignancy), operative time, and use of negative-pressure wound therapy. These results will help to inform patients and surgeons regarding the risk of reoperation by diagnosis and reinforce operative time as a factor influencing reoperation. These results also support further investigation into the use of negative-pressure wound therapy at the time of surgery in this patient population. LEVEL OF EVIDENCE: Therapeutic Level II. See Instructions for Authors for a complete description of levels of evidence.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.053
GPT teacher head0.284
Teacher spread0.231 · 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".

Quick stats

Citations7
Published2023
Admission routes1
Has abstractyes

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