ArticleSurgery today2023
Machine learning models in clinical practice for the prediction of postoperative complications after major abdominal surgery.
Article in Surgery today, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05810207 (The External Validation of a Machine Learning Model Predicting Anastomotic Leakage Intraoperatively in Patients Undergoing a Colorectal Resection - A1Check Study), which is not on this map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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.
The External Validation of a Machine Learning Model Predicting Anastomotic Leakage Intraoperatively in Patients Undergoing a Colorectal Resection - A1Check Study: Protocol for a Multicenter Observational Study
Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning in the development and application of patient-reported outcome measures (PROMs) for surgical patients: a systematic review.Journal of patient-reported outcomes · 2026Pooled it
- Bringing AI to the OR: integrating a machine learning predictive model in the EHR - a pilot on user-friendliness.BMJ health & care informatics · 2026Article
- Cachexia index as a prognostic marker in patients undergoing biliary tract cancer resection.Surgery today · 2025Article
- Patient perspectives on AI-based decision support in surgery.BMJ surgery, interventions, & health technologies · 2025Article
- Artificial Intelligence, the Digital Surgeon: Unravelling Its Emerging Footprint in Healthcare - The Narrative Review.Journal of multidisciplinary healthcare · 2024Review
- Machine learning perioperative applications in visceral surgery: a narrative review.Frontiers in surgery · 2024Review
- Predicting postoperative adhesive small bowel obstruction in infants under 3 months with intestinal malrotation: a random forest approach.Jornal de pediatriaArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Complications after surgery have a major impact on short- and long-term outcomes, and decades of technological advancement have not yet led to the eradication of their risk. The accurate prediction of complications, recently enhanced by the development of machine learning algorithms, has the potential to completely reshape surgical patient management. In this paper, we reflect on multiple issues facing the implementation of machine learning, from the development to the actual implementation of machine learning models in daily clinical practice, providing suggestions on the use of machine learning models for predicting postoperative complications after major abdominal surgery.
Indexed as
Identifiers
What Socratic 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 Socratic graph.