ReviewThe American surgeon2023
Predicting Patient-Reported Outcomes Following Surgery Using Machine Learning.
Review in The American surgeon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Development of machine learning models for preoperative prediction of insufficient clinical improvement after anterior cruciate ligament reconstruction.BMC musculoskeletal disorders · 2026Article
- Differences in patient-reported symptoms following radical gastrectomy: a propensity score-matched cohort study.Frontiers in oncology · 2026Article
- The role of AI for improved management of breast cancer: Enhanced diagnosis and health disparity mitigation.Computer methods and programs in biomedicine · 2025Review
- Review
- Artificial Intelligence Applications in Decision-Making for Disease Management:Sultan Qaboos University medical journal · 2025Article
- International expert consensus on the current status and future prospects of artificial intelligence in metabolic and bariatric surgery.Scientific reports · 2025Article
- Pharmacist-led surgical medicines prescription optimization and prediction service improves patient outcomes - a machine learning based study.Frontiers in pharmacology · 2025Article
- Digital Information Ecosystems in Modern Care Coordination and Patient Care Pathways and the Challenges and Opportunities for AI Solutions.Journal of medical Internet research · 2024Article
- Review
- Multimodal machine learning for distinguishing pediatric multiple sclerosis from non-inflammatory conditions using optical coherence tomography.Frontiers in neurologyArticle
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
Abstract
Patient-reported outcomes (PROs) enable providers to identify differences in treatment effectiveness, postoperative recovery, quality of life, and patient satisfaction. By allowing a shift from disease-specific factors to the patient perspective, PROs provide a tailored patient-centric approach to shared decision-making. Artificial intelligence (AI) and machine learning (ML) techniques can facilitate such shared decision-making and improve patient outcomes by accurate prediction of PROs. This article aims to provide a comprehensive review of the use of AI and ML models in predicting PROs following surgery through an overview of common predictive algorithms and modeling techniques, as well as current applications and limitations in the surgical field.
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.