Does rain really cause pain? A systematic review of the associations between weather factors and severity of pain in people with rheumatoid arthritis
Bibliographic record
Abstract
OBJECTIVE: To examine the association between weather and pain in rheumatoid arthritis (RA). METHODS: Systematic review of longitudinal observational studies (up to September 2009) with data on the association between weather variables and severity of pain in RA. The methodological quality was rated independently by the two authors according to an adapted Newcastle-Ottawa Scale. We analyzed the data on an aggregated (group) level with a meta-analysis of correlations between pain and weather, and at an individual level as the proportion of patients for whom pain was significantly affected by the weather. RESULTS: Nine studies were included. Many different weather variables have been studied, but only three (temperature, relative humidity and atmospheric pressure) have been studied extensively. Overall group level analyses show that associations between pain and these three variables are close to zero. Individual analyses from two studies indicate that pain reporting in a minority (<25%) of RA patients is influenced by temperature, relative humidity or atmospheric pressure. We were not able to relate the findings to methodological quality or other aspects of the studies. CONCLUSION: The studies to date do not show any consistent group effect of weather conditions on pain in people with RA. There is, however, evidence suggesting that pain in some individuals is more affected by the weather than in others, and that patients react in different ways to the weather. Thus, the hypothesis that weather changes might significantly influence pain reporting in clinical care and research in some patients with RA cannot be rejected.
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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 | 0.010 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".