Healthy dietary indices and noncancer pain: a systematic review of cross-sectional and longitudinal studies
Bibliographic record
Abstract
ABSTRACT: Pain is a global public health problem given its high prevalence and incidence, long duration, and social and economic impact. There is growing interest in nutrition as potential modifiable risk factor related to pain; however, the associations between healthy dietary patterns and pain have not yet been well established. Thus, we aimed to systematically review and synthesise current cross-sectional and longitudinal evidence on the relationship between a priori healthy dietary patterns and noncancer pain among adults aged ≥18 years. We identified relevant published cross-sectional and longitudinal studies by systematically searching several electronic databases from inception to September 2021. Risk of bias was assessed using the modified Newcastle-Ottawa scale for cohort studies. A total of 14 cross-sectional and 6 longitudinal studies were included in the review. These studies measured different dietary scores/indices, such as different measures of adherence to the Mediterranean diet and the dietary inflammatory index. Pain ascertainment methods and pain measurements used differed across studies. All 20 of the included studies had different study designs and statistical analysis. Of these studies, 10 reported an inverse association between adherence to a healthy dietary pattern and pain, 5 reported mixed results, and 5 reported no associations. Despite notable heterogeneity, 50% of included observational studies reported that adherence to a healthy diet, particularly the Mediterranean diet, is inversely associated with pain. Of note, the cross-sectional design of most studies precludes any causal interpretation. Moreover, limited and inconsistent evidence from longitudinal studies highlights the need for further studies.
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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 | 0.016 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".