Is there an association between vitamin D status and risk of chronic low back pain? A nested case–control analysis in the Nord-Trøndelag Health Study
Bibliographic record
Abstract
OBJECTIVES: To explore potential associations between vitamin D status and risk of chronic low back pain (LBP) in a Norwegian cohort, and to investigate whether relationships depend on the season of blood sample collection. DESIGN: A nested case-control study in a prospective data set. SETTING: The Norwegian community-based Nord-Trøndelag Health Study (HUNT). Data were collected in the HUNT2 (1995-1997) and HUNT3 (2006-2008) surveys. MAIN OUTCOME MEASURE: Chronic LBP, defined as LBP persisting at least 3 months continuously during the past year. PARTICIPANTS: Among individuals aged 19-55 years without LBP in HUNT2, a data set was generated including 1685 cases with LBP in HUNT3 and 3137 controls without LBP. METHODS: Blood samples from the participants collected in HUNT2 were analysed for serum 25-hydroxyvitamin D (25(OH)D) level. Associations with LBP in HUNT3 were evaluated by unconditional logistic regression analysis with adjustment for age, sex, work status, physical activity at work and in leisure time, education, smoking, and body mass index. RESULTS: No association between vitamin D status and risk of chronic LBP was found in the total data set (OR per 10 nmol/L 25(OH)D=1.01, 95% CI 0.97 to 1.06) or in individuals with blood samples collected in summer/autumn (OR per 10 nmol/L 25(OH)D=0.99, 95% CI 0.93 to 1.06). For blood samples drawn in winter/spring, associations differed significantly between women and men (p=0.004). Among women a positive association was seen (OR per 10 nmol/L 25(OH)D=1.11, 95% CI 1.02 to 1.20), but among men no significant association was observed (OR per 10 nmol/L 25(OH)D=0.90, 95% CI 0.81 to 1.01). CONCLUSIONS: Overall, no association between vitamin D status and risk of LBP was demonstrated. The association suggested in women for the winter/spring season cannot be regarded as established.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".