ArticlePain reports2026
Predicting high health care use in patients with spinal disorder in secondary care: model development and validation.
Article in Pain reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Spinal disorders are among the biggest contributors to health care utilization (HCU). Objective: To develop and externally validate a prediction model for high all-cause HCU (75th percentile during 1 year after the index date) among patients with spinal disorders visiting multidisciplinary secondary care clinics. Methods: We developed and internally validated the model using the Norwegian Neck and Back Registry, including patients registered between January 1, 2016, and December 31, 2020, linked with national health registries (N = 9092). For external validation, we used data from the Danish SpineData Registry, linked with national registries, for the same period (N = 34,853). We assessed Nagelkerke Results: The final model included sex, nationality, education, physical activity, smoking, prior HCU, work status, disability, health-related quality of life, medicine use, diagnosis, kinesiophobia, and comorbidity. It demonstrated acceptable discrimination (AUC 0.78, 95% confidence interval [CI], 0.77-0.78), an Conclusion: Overall, the model shows promise in predicting high HCU in patients with spinal disorders referred to secondary care but requires further testing and validation in implementation settings before recommendation.
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