ArticleThe journal of pain2026
Predicting the course of high-impact chronic pain using machine learning algorithms.
Article in The journal of pain, 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
17 authors.
Funding
Abstract
High-impact chronic pain (HICP) affects over 17 million U.S. adults and follows highly variable courses. To date, the relative importance of biopsychosocial predictors of HICP incidence, persistence, and recovery, remains poorly understood. The National Health Interview Survey Longitudinal Cohort, which comprises 10,415 adults who completed baseline surveys in 2019 and follow-back surveys in 2020, is ideally suited for addressing this critical knowledge gap. Machine learning algorithms were evaluated for their discriminatory power and classification accuracy in predicting HICP group membership for eligible sample adults who reported pain at one or both time points (n=10,260): incident HICP (new onset in 2020; n=506), persistent HICP (present in both years; n=480), HICP recovery (present in 2019 only; n=471), or no HICP (neither year; n=8803). Shapley Additive Explanation values were generated for input features to assess their relative importance for model prediction. Gradient Boosting Decision Trees, which exhibited the highest discriminatory power (macro-AUC=0.80), revealed that family income was a strong predictor of both incident HICP and HICP recovery, while physical health (e.g., self-rated health, arthritis, prescription opioid use) was a key predictor of persistent HICP. Depression severity was the most important mental health predictor across all outcomes. This study highlights the relative prognostic importance of physical health, mental health, and socioeconomic factors for HICP over a one-year follow-up. These findings can help to advance predictive frameworks for HICP, refine clinical risk screening tools, and set the stage for future research evaluating modifiable risk and resilience factors to prevent or alleviate HICP. PERSPECTIVE: This study used a machine learning approach to reveal differential prognostic importance of biopsychosocial factors for high-impact chronic pain incidence, persistence, and recovery over a one-year follow-up. These findings could help improve clinical risk screening tools and inform future targeted interventions to prevent or alleviate high-impact chronic pain.
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.