ArticleInternational endodontic journal2026
Restoration's Longevity in Endodontically Treated Teeth: A Machine Learning Survival Analysis From Randomised Clinical Trials.
Article in International endodontic journal, 2026. 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
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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.
- Machine Learning Models for Identifying Dental Pain in Adolescents.International dental journal · 2026Article
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Authors and funding
8 authors.
Funding
Abstract
aimThis prognostic study aims to develop a machine learning (ML) survival model for estimating the longevity (success and survival rate) of restorations in endodontically treated teeth (ETT). METHODOLOGY: Data were consolidated from four controlled clinical trials conducted in the Netherlands and Brazil, involving 424 patients and 618 restorations with up to 17 years of follow-up. The evaluated predictive models included Gradient Boosting Survival, Random Survival Forests and Survival Support Vector Machine. The dataset was split into 70% for training and 30% for testing. Hyperparameter tuning was optimised via 10-fold cross-validation with 50 iterations using hyperopt. Performance was assessed through the time-dependent area under the ROC curve (AUC), concordance index (C-index), inverse probability of censoring weights (IPCW C-index) and time-dependent Brier score.
resultsThe Gradient Boosting Survival model achieved the highest AUC mean (0.83, 95% confidence interval [CI], 0.81-0.78), C-index (0.80), IPCW C-index (0.78) and Brier score (0.06) for survival rate predictions, maintaining predictive stability over time. For success rate, the Random Survival Forest model outperformed others (AUC = 0.73, 95% CI [0.70-0.75]), C-index (0.66), IPCW C-index (0.64) and Brier score (0.14). SHAP analysis identified patient age and tooth type as having the highest variable importance for survival, while the dentist's experience was critical for success outcomes. Fairness analysis revealed performance disparities across sexes and countries in the models.
conclusionsThe models demonstrated high predictive performance, mainly in survival rate prediction. ML models show promise for developing a robust, data-driven framework to evaluate success and survival outcomes in ETT.
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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.