ArticleFuture healthcare journal2026
Multistage machine learning model for automated referral triage in pain medicine.
Article in Future healthcare journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
Effective referral triage in pain medicine is essential to ensure that patients receive timely and appropriate care. This study presents a multistage machine learning framework to better identify patients who may benefit from one of five specialised pain procedures. Two years of clinic data were used for training and validation, consisting of 231 features for 3,552 patients. The proposed approach started with the baseline model selection, followed by stage iterations acquiring the elbow method for stage decision. The Easy Ensemble was selected among methods and applied for each stage. Results showed that the approach improves prediction accuracy in the true positive rate (TPR) and area under the curve (AUC). The final-stage models achieved the improvement over single-stage model by as much as 34.8% and 23% on TPR and AUC. This multistage framework can enhance triage accuracy and hold potential for broader application in other clinical settings.
Indexed as
Identifiers
What Socratic 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 Socratic graph.