ArticleJournal of orthopaedics2025
Predictive value of the 5-item modified frailty index in adverse outcomes in total elbow arthroplasty: A machine-learning model analysis.
Article in Journal of orthopaedics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Artificial Intelligence in Arthroplasty: A Comprehensive Structured Critical Review and Descriptive Evidence Map of Validation, Uncertainty, and Workflow Integration.Bioengineering (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: The modified frailty index (mFI-5) is a five factor risk stratification tool predicated on functional status and key medical comorbidities. mFI-5 scores have already demonstrated potential for predicting adverse outcomes after common orthopaedic procedures. The aim of this study was to capitalize upon this potential and leverage machine learning analysis to (1) further interrogate the utility of the mFI-5 as a risk stratification tool, and (2) develop an algorithm with predictive value for adverse outcomes after total elbow arthroplasty (TEA). Methods: A retrospective review of patients who underwent TEA from 2010 to 2020 was conducted using the American College of Surgeons National Surgery Quality Improvement Program (NSQIP) database. Postoperative complications were analyzed as summative binary variables representing the rate of complications such as mortality, readmission, reoperation, extended hospital length of stay (LOS), and discharge to a non-home destination. Univariate and multivariate analysis were performed to determine the relationship between mFI-5 and postoperative complications at the p < 0.05 level. An XGBoost binary classifier was trained to predict significant associations identified in the multivariate regression. SHAP model explainability determined the relative importance of each mFI-5 component. Results: A total of 725 patients (mean age 65.7 Conclusion: mFI-5 scores have high predictive value for longer hospital stays and non-home discharge after TEA. There is growing evidence for using frailty indices to stratify risk and the model developed in this study may serve as a prototype for future clinical decision-making tools.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.