ArticleBMJ open2025
Prognostic machine learning models for predicting postoperative complications following general surgery in Bandar Abbas, Iran: a study protocol.
Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Development and interpretability of a machine learning model for postoperative hypothermia prediction in placenta previa Cesarean section: A retrospective cohort study.The Journal of international medical research · 2026Article
- Machine Learning-Based Prediction of Fetal Macrosomia Using Maternal: A Pilot Study.Diagnostics (Basel, Switzerland) · 2026Article
- Development and external validation of machine learning models to predict insulin resistance among iron-deficient children and adolescents.Translational pediatrics · 2026Article
- The power of machine learning models in predicting gestational diabetes mellitus.BMC pregnancy and childbirth · 2026Article
- Risk stratification for postoperative complications after CRS and HIPEC in recurrent ovarian cancer patients: a comparative analysis of logistic regression and machine learning models.Scientific reports · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionTo enhance the quality of surgical care, complications need to be minimised. Consequently, comprehending the occurrence and risk elements for postoperative complications is essential. Subsequently, we will apply machine learning (ML) algorithms to build risk factor prediction models that will assist surgeons in identifying the risk factors associated with the development of postoperative complications after general surgery. METHODS AND ANALYSIS: This research will take place at a tertiary referral medical centre located in Bandar Abbas, Hormozgan, Iran. The inclusion criteria are (1) individuals aged 18 years or older who have any type of general surgery and (2) hospitalised from September 2025 to September 2026. Individuals with insufficient data will be excluded. The main outcomes of the study are complications within 30 days of surgery. Patients will be divided into two groups based on whether they develop complications or not. The predictors are classified as (1) patient-related factors, (2) surgery-related factors and (3) postoperative factors. We intend to detect postoperative complications following general surgery using four distinct supervised ML techniques: (1) logistic regression, (2) decision trees, (3) random forests and (4) extreme gradient boosting. Accuracy, precision, recall and F1 score will be used to evaluate the performance of ML models. ETHICS AND DISSEMINATION: With approval from the Hormozgan University of Medical School Research Ethics Board (IR.HUMS.REC.1404.137), we will carry out a forward-looking analysis of the medical records of patients undergoing general surgery. We will obtain informed consent, and all information will be collected and examined anonymously. The findings of this research will be released in appropriate scientific publications.
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.