MétaCan
Menu
Back to cohort
Record W4206605113 · doi:10.1016/j.artd.2021.10.007

Revision Total Hip Arthroplasty in Solid Organ Transplant Patients: A Propensity Score-Matched Cohort Study for Aseptic and Infected Revisions

2022· article· en· W4206605113 on OpenAlexaff
Alex Upfill-Brown, Christopher M. Hart, Peter P. Hsiue, Kadarius Burgess, Clark J. Chen, Amir Khoshbin, Christos Photopoulos, Alexandra I. Stavrakis

Bibliographic record

VenueArthroplasty Today · 2022
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Infections and Treatments
Canadian institutionsUniversity of Toronto
FundersNational Institute of Arthritis and Musculoskeletal and Skin Diseases
KeywordsMedicinePropensity score matchingTotal hip arthroplastyAseptic processingCohortInternal medicineRetrospective cohort studyArthroplastySignificant differenceSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Previous studies have demonstrated that solid organ transplant (SOT) patients undergoing primary total hip arthroplasty (THA) are at an increased risk of postoperative complications. The purpose of this study is to use a large, national database to investigate revision THA (rTHA) outcomes in SOT patients. METHODS: Nationwide Readmissions Database (NRD) from 2010-2018 was used, and ICD-9 and ICD-10 codes were used to identify all patients who underwent rTHA, including those with history of SOT. Propensity score matching (PSM) was used to analyze rTHA outcomes in SOT patients comparted to matched controls. Separate analysis performed for patients undergoing rTHA for prosthetic joint infection (PJI) vs other causes. RESULTS: = .63). CONCLUSIONS: SOT patients undergoing rTHA for aseptic reasons are higher risk of overall readmission but lower risk of readmission related to rTHA than appropriately matched controls. SOT PJI patients undergoing had similar rates of readmission, mortality, and revision surgery compared to matched non-SOT PJI patients.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.261
Teacher spread0.245 · 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.

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

Citations10
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueArthroplasty TodaySame topicOrthopedic Infections and TreatmentsFrench-language works237,207