ArticleNeurosurgery practice2025
Comparison of Machine Learning Methods to Predict Early Mortality After Evacuation of Chronic Subdural Hematoma.
Article in Neurosurgery practice, 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.
- Predictors of Six-Month Functional Outcome After Chronic Subdural Hematoma Surgery: Logistic Regression Versus Machine Learning.Journal of clinical medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectivesWe developed a series of machine learning models to predict early mortality after chronic subdural hematoma (cSDH) evacuation.
methodsWe retrospectively collected patients treated surgically for cSDH at 4 level 1 trauma centers (2009-2021). Previously, we developed a deep learning segmentation tool to automatically calculate preoperative and postoperative cSDH volumes. Using cSDH volumes and clinical information, we developed 6 machine learning models including logistic regression (LR), support vector machine, neural network (NN), decision tree (DT), Naïve Bayes, and XGBoost to predict 30-day mortality after surgery. We applied least absolute shrinkage and selection operator regression to select a subset of predictors for consistent model input. To account for class imbalance, we used synthetic minority oversampling technique. We used 10-fold cross validation to evaluate model performance.
resultsWe included 731 patients. Our final models included age, admission Glasgow Coma Scale, unilateral/bilateral hematoma, antiplatelet status, platelet count, preoperative volume, and method of surgical evacuation. The 30-day mortality rate was 7.5%. Overall, our models demonstrated moderate discriminative ability with area under the receiver operating characteristics curves (AUCs) ranging from 0.64 for DT (95% CI: 0.56-0.72) to 0.75 for LR (95% CI: 0.69-0.81). AUC for DT was significantly lower than LR (
conclusionThe LR model demonstrated the best performance of discriminative ability, balanced accuracy, and recall, whereas DT modeling performed worst. Using an automated segmentation software, our models demonstrate an ability to identify patients at high risk of mortality after treatment for cSDH.
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.