Early life chronic inflammatory conditions predict low back pain in adolescence and young adulthood
Bibliographic record
Abstract
BACKGROUND: Associations between inflammatory conditions and low back pain (LBP) have been found frequently in older populations. However, the nature of these relationships in younger populations is unknown. This study aimed to investigate the associations between early life chronic or recurrent inflammatory conditions and impactful LBP in adolescence and young adulthood. METHODS: In this longitudinal study, we used data from the Raine Study Gen2 participants at the 1, 2, 3, 5, 8, 10, 14, 17, 20 and 22-year follow-ups (N = 2,868). Data were collected on inflammatory conditions from 1 to 22 years of age and occurrences of impactful LBP from 14 to 22 years of age. Longitudinal and cross-sectional associations between inflammatory conditions and impactful LBP occurrence were examined. Potential dose-response relationships between the number of inflammatory conditions and impactful LBP were also assessed. Logistic regression models were used in the analysis. RESULTS: Participants with respiratory or atopic conditions during childhood had increased odds of future impactful LBP in adolescence and young adulthood (odds ratio (OR) [95% confidence interval (CI)] = 1.29 [1.07, 1.54] and 1.23 [1.02, 1.49], respectively). There were cross-sectional associations between inflammatory conditions including respiratory, skin, musculoskeletal, autoimmune and atopic conditions, with impactful LBP. Participants with two illnesses and three or more illnesses had an increased odds (OR [95% CI] =1.68 [1.30, 2.18] and OR [95% CI] =2.12 [1.54, 2.89], respectively) of reporting impactful LBP. CONCLUSIONS: Overall, longitudinal and cross-sectional associations of respiratory and atopic conditions with impactful LBP in adolescence and young adulthood were identified. More evidence is needed to determine whether there is a causal relationship between chronic inflammatory conditions and impactful LBP. SIGNIFICANCE: Low back pain (LBP) is a prominent and significant health problem and associations between inflammatory conditions and LBP have been found frequently in older populations. We found that children with respiratory or atopic conditions and those with several chronic inflammatory conditions are at increased odds of impactful LBP in adolescence and young adulthood. In clinical practice and future research, there is a need to consider comorbidities also in younger populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".