ArticleFrontiers in medicine2026
Preoperative risk stratification for postoperative inflammatory-nutritional deterioration after colorectal cancer surgery: an interpretable machine learning study.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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: Postoperative inflammatory-nutritional deterioration may adversely affect recovery after colorectal cancer surgery. This study aimed to develop and internally validate an interpretable machine learning model for preoperative prediction of postoperative modified Glasgow Prognostic Score (mGPS) = 2. Methods: This single-centre retrospective study included consecutive patients who underwent elective radical resection for colorectal cancer between 2020 and 2025. The cohort was divided into training and internal validation sets using stratified sampling. Recursive feature elimination identified eight predictors: age, prealbumin, albumin, cholinesterase, lymphocyte percentage, platelet count, fibrinogen and serum calcium. Eight conventional machine learning algorithms, TabICLv2 and SuperLearner ensembles were evaluated using discrimination, calibration and decision curve analyses. SHapley Additive exPlanations were used for model interpretation. Results: Among 1196 patients, 507 (42.4%) developed postoperative mGPS=2. In the internal validation cohort, TabICLv2 achieved an area under the receiver operating characteristic curve of 0.753 (95% confidence interval, 0.701-0.804) and a Brier score of 0.197. SuperLearner-W and SuperLearner-SR showed comparable discrimination, with areas under the curve of 0.752 and 0.751, respectively. Lymphocyte percentage, platelet count and fibrinogen were the leading contributors to model predictions. Learning-curve analysis revealed suboptimal convergence behaviour, with relatively flat cross-validation trajectories and persistent separation from training performance across increasing training fractions. A web-based research prototype was developed to provide individualised risk estimates, patient-level feature attribution and an applicability-domain assessment indicating the level of support from the model-development data. Conclusion: An interpretable model based on routinely available preoperative variables provided moderate discrimination for postoperative inflammatory-nutritional deterioration after colorectal cancer surgery. The suboptimal learning-curve convergence indicates that the model is not suitable for clinical deployment without further development and independent external validation.
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.