ArticleThe Korean journal of pain2020
A prediction model of low back pain risk: a population based cohort study in Korea.
Article in The Korean journal of pain, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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
12 citing papers in PubMed, 18 citations in OpenAlex.
- Familial clustering and physiological risk factors associated with intervertebral disc degeneration in a large population-based cohort.Osteoarthritis and cartilage open · 2026Article
- Spinal morphological change after posterior lumbar interbody fusion in lumbar spondylolisthesis patients.Medicine · 2025Observational
- Overweight and obesity as risk factors for chronic low back pain: a new follow-up in the HUNT Study.BMC public health · 2024Article
- Prediction of Back Disability Using Clinical, Functional, and Biomechanical Variables in Adults with Chronic Nonspecific Low Back Pain.Journal of clinical medicine · 2024Article
- Article
- Body composition parameters as predictors of low back pain in rural homemakers of North India.Journal of family medicine and primary care · 2024Article
- Predictive Classification System for Low Back Pain Based on Unsupervised Clustering.Global spine journal · 2023Article
- The evolution of Big Data in neuroscience and neurology.Journal of big data · 2023Article
- Development, Pathogenesis, and Regeneration of the Intervertebral Disc: Current and Future Insights Spanning Traditional to Omics Methods.Frontiers in cell and developmental biology · 2022Review
- Predicting Physician Consultations for Low Back Pain Using Claims Data and Population-Based Cohort Data-An Interpretable Machine Learning Approach.International journal of environmental research and public health · 2021Article
- Machine Learning Approaches to Predict Chronic Lower Back Pain in People Aged over 50 Years.Medicina (Kaunas, Lithuania) · 2021Article
- Automated Smart Home Assessment to Support Pain Management: Multiple Methods Analysis.Journal of medical Internet research · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWell-validated risk prediction models help to identify individuals at high risk of diseases and suggest preventive measures. A recent systematic review reported lack of validated prediction models for low back pain (LBP). We aimed to develop prediction models to estimate the 8-year risk of developing LBP and its recurrence.
methodsA population based prospective cohort study using data from 435,968 participants in the National Health Insurance Service-National Sample Cohort enrolled from 2002 to 2010. We used Cox proportional hazards models.
resultsDuring median follow-up period of 8.4 years, there were 143,396 (32.9%) first onset LBP cases. The prediction model of first onset consisted of age, sex, income grade, alcohol consumption, physical exercise, body mass index (BMI), total cholesterol, blood pressure, and medical history of diseases. The model of 5-year recurrence risk was comprised of age, sex, income grade, BMI, length of prescription, and medical history of diseases. The Harrell's C-statistic was 0.812 (95% confidence interval [CI], 0.804-0.820) and 0.916 (95% CI, 0.907-0.924) in validation cohorts of LBP onset and recurrence models, respectively. Age, disc degeneration, and sex conferred the highest risk points for onset, whereas age, spondylolisthesis, and disc degeneration conferred the highest risk for recurrence.
conclusionsLBP risk prediction models and simplified risk scores have been developed and validated using data from general medical practice. This study also offers an opportunity for external validation and updating of the models by incorporating other risk predictors in other settings, especially in this era of precision medicine.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.