ArticleNeurosurgery practice2024
Machine Learning for Individualized Risk Estimation in Anterior Lumbar Interbody Fusion.
Article in Neurosurgery practice, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- AI Risk Prediction Tools for Autologous Breast Reconstruction.Journal of surgical oncology · 2026Article
- Embracing Large Language Models for Medical Applications, Part II: Building a Framework for Clinical Stewardship.Cureus · 2026Article
- Using Machine Learning to Predict Breast Cancer-Related Lymphedema Following Axillary Lymph Node Dissection.Annals of surgical oncology · 2026Article
- Predicting Breast Reconstruction Readmission, Reoperation, and Prolonged Length of Stay: A Machine Learning Approach.Journal of surgical oncology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectivesAlthough the anterior approach to the spine for anterior lumbar interbody fusion (ALIF) has been shown to be an effective procedure, there are different surgical risks compared with conventional posterior fusion. ALIF patients could potentially receive more personalized care plans that minimize the risk of negative outcomes by forecasting short-term postoperative results before the surgical procedure. The objective of this research was to evaluate the performance of machine learning (ML) algorithms in predicting short-term unfavorable postoperative outcomes after ALIF and to develop an easy-to-use and readily available instrument for this purpose.
methodsUsing the American College of Surgeons National Surgical Quality Improvement Program database, we identified ALIF patients and used 6 ML algorithms to build models predicting postoperative outcomes. These models were then incorporated into an open-access web application.
resultsThe analysis included 8304 ALIF patients. The LightGBM models achieved area under the receiver operating characteristic scores of 0.735 for prolonged length of stay and 0.814 for nonhome discharges. The random forest models achieved area under the receiver operating characteristics of 0.707 for 30-day readmissions and 0.701 for major complications. These top-performing models were integrated into a web application for individualized patient predictions.
conclusionML techniques show promise in predicting postoperative outcomes for ALIF surgeries. As data in spinal surgery expand, these predictive models could significantly improve risk assessment and prognosis. We present an accessible predictive tool for ALIF surgeries to achieve the goals mentioned above.
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.