aceMachine Learning Prediction of Anterior Open-Bite Development After Stabilization Splint Treatment in Temporomandibular Disorder
Objectives To develop interpretable machine learning (ML) models for risk stratification of clinically meaningful anterior overbite decrease after stabilization splint therapy in temporomandibular disorder (TMD) patients, and to compare ML performance with conventional logistic regression. Materials and methods This retrospective cohort included 87 TMD patients treated with stabilization splints, physical therapy, and pharmacologic therapy. Thirty-five pre-treatment cephalometric and demographic features were us…
Interpretable machine learning on pre-treatment cephalometric data separated higher-risk from lower-risk TMD patients better than logistic regression, which could support counseling and closer monitoring.
Regression models had near-zero or negative cross-validated R2 values, so exact overbite change could not be predicted from pre-treatment cephalometrics alone. The cohort was also small and retrospective.
Evidence
- Peer-reviewedClinical and Experimental Dental Research2026-10-01
How should this claim be treated?
Truvace Impact Record TRV-2026-1570, v1: “Machine Learning Prediction of Anterior Open-Bite Development After Stabilization Splint Treatment in Temporomandibular Disorder.” Truvace, 2026-10-16. /record/TRV-2026-1570 (accessed at citation time). sha256 9c4e455e8eb57d71…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-1570 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.