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TRUVACE RECORD VERSION
record: TRV-2026-2124
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-21T22:02:09.004124Z
status: published
lens: g_space
sector: health
headline: An Explainable Prediction Model for Outcomes After Anterior Subcutaneous Ulnar Nerve Transposition in Idiopathic Cubital Tunnel Syndrome
dek: To identify preoperative clinical and electrodiagnostic predictors of outcome after anterior subcutaneous ulnar nerve transposition for idiopathic cubital tunnel syndrome and to develop an explainable model for individualized prognosis. This retrospective cohort study included 119 patients who underwent anterior subcutaneous ulnar nerve transposition for idiopathic cubital tunnel syndrome between 2005 and 2022 with at least 1 year of follow-up. Candidate predictors included demographic factors, occupational cate…
gain_title: An explainable extremely randomized trees model identified wrist-level compound muscle action potential amplitude and preoperative severity grade as key predictors, which may support individualized preoperative counseling.
problem_title: (none)
trace_subject: (none)
gain_reading: An explainable extremely randomized trees model identified wrist-level compound muscle action potential amplitude and preoperative severity grade as key predictors, which may support individualized preoperative counseling.
gain_evidence: Explainable prediction modeling may support individualized preoperative counseling. | Wrist-level compound muscle action potential amplitude and modified McGowan grade were the dominant prognostic features.
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers reviewed 119 patients who had anterior subcutaneous ulnar nerve transposition for idiopathic cubital tunnel syndrome between 2005 and 2022. They compared conventional regression with six machine-learning algorithms to predict whether recovery would be excellent or good versus fair or poor.

The work suggests that nerve test results and clinical severity could help surgeons tell patients what recovery to expect before surgery. The findings come from a single retrospective cohort with internal validation only, and the thresholds are described as exploratory, so real-world clinical use remains unproven.
limitation: The thresholds come from exploratory model interpretation in a retrospective cohort, and the machine-learning models were only internally validated, so their performance in other patients is untested.
tag: Evidence-backed gain
key_points: Sixty-eight patients (57.1%) had an excellent or good outcome, while 51 (42.9%) had a fair or poor outcome. | The extremely randomized trees model had the highest mean AUC and lowest Brier score among the models tested. | Wrist-level compound muscle action potential amplitude and modified McGowan grade were the dominant predictors. | Exploratory reference values were amplitude of at least 6.6 mV and McGowan grade of IIA or lower.
rundown: The retrospective cohort required at least one year of follow-up. Candidate predictors included demographics, occupational category score, elbow carrying angle, modified McGowan grade, and electrodiagnostic parameters.

Outcomes were graded with Messina's criteria and dichotomized for modeling. The machine-learning models were internally validated with nested stratified 5-fold cross-validation repeated 50 times.

Performance was judged by area under the receiver operating characteristic curve, F1 score, and Brier score. Interpretation used feature importance and local interpretable model-agnostic explanations.
sources:
- peer_reviewed | The Journal of Hand Surgery | https://doi.org/10.1016/j.jhsa.2026.08.013 | 2026-10-19
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