ArticleJournal of pain research2025
Machine Learning Analysis to Identify Predictive Factors of Caudal Epidural Pulse Radiofrequency in the Treatment of Coccygodynia.
Article in Journal of pain research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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, 1 synthesis or guideline pooled it.
- Artificial intelligence for prediction of clinical response and therapeutic value in interventional pain management: a scoping review.Frontiers in digital health · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: This study aims to use machine learning (ML) to explore predictive parameters related to the efficacy of caudal epidural pulsed radiofrequency (CEPRF) treatment for coccygodynia. Methods: Five different ML methods were used to predict treatment success at 6 months after CEPRF. The findings generated by these algorithms are compared with respect to the accuracy of the results. Results: Symptom duration, angular deformation and NRS at admission are the most significant factors impacting therapy success in coccygodynia patients. Success rates are obtained for relatively short symptom durations to be 71.83%, for longer periods to be 16.67%; for short durations together with no angular deformity to be 79.55%, with angular deformity to be 59.26%; and for NRS level at admission less than 8 together with angular deformity to be 91.67%, with no angular deformity to be 33.33%. Conclusion: This research reveals the potential of ML methods to improve treatment outcome prediction in coccygodynia. When a new patient is admitted, the ML-generated decision trees provide a quick and precise assessment of the possible success rate of CEPRF treatment.
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Registered trials
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