ArticlePloS one2025
Predicting the risk of postoperative constipation in middle-aged and elderly patients with lower limb fractures using machine learning algorithms.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Constipation in Older Adults: Pathophysiology, Clinical Impact, and Management Strategies.Geriatrics (Basel, Switzerland) · 2026Review
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
objectiveTo construct and validate a predictive model for the risk of postoperative constipation in middle-aged and elderly patients with lower limb fractures based on machine learning algorithms, so as to provide decision-making support for clinical prevention and early intervention.
methodsThis study conducted a retrospective analysis of clinical data of 1,128 middle-aged and elderly patients who underwent lower limb fracture surgery between January 2020 and May 2024, with data collection occurring from October to December 2024. Whether constipation occurred or not was used as the outcome variable. Eight machine learning algorithms, namely logistic regression (LR), extreme gradient boosting (XGBoost), random forest (RF), decision tree classifier (DT), complement naive bayes (CNB), multilayer perceptron (MLP), support vector machine (SVM), and K-Nearest neighbors (KNN), were employed to construct predictive models. Key risk factors were identified using SHAP (SHapley Additive exPlanations), a game theory-based approach for analyzing feature importance. Model predictive performance was comprehensively evaluated using metrics including the area under the receiver operating characteristic curve (AUC), accuracy, and other relevant indicators.
resultsThe logistic regression (LR) model demonstrated the optimal predictive performance. Age, femoral fracture, length of hospital stay, nutritional risk, and chronic gastritis were identified as important predictive factors. This model can be integrated into the clinical information system to automatically flag high-risk patients upon admission and provide individualized interventions based on risk stratification.
conclusionThe logistic regression (LR) model developed in this study exhibits strong discriminative ability and clinical utility, enabling dynamic perioperative monitoring of constipation risk through digital health tools, thereby potentially reducing related complications.
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.