The Influence of Chinook Winds and Other Weather Patterns upon Neuropathic Pain
Bibliographic record
Abstract
OBJECTIVE: Although Chinook winds are often viewed positively during a cold prairie winter, patients suffering with neuropathic pain (NeP) anecdotally report exacerbations of NeP during Chinooks and during other weather changes. Our objective was to identify if Chinook winds lead to acute exacerbations in pain severity in a NeP patient population. DESIGN: Prospective diary-based assessments of patients with at least moderate NeP over 6-month periods during different seasons of the year were performed. Concurrent weather conditions were tracked hourly, with Chinook winds defined using accepted meteorological definition. We also examined other aspects of weather including precipitation, temperature, and humidity. Days with acute exacerbations were defined when a daily visual analog score pain score was ≥2 points above their average NeP score over the entire 6-month period. RESULTS: Chinooks were not associated with individual acute exacerbations in NeP. Instead, Chinook days were found to be protective against acute exacerbations in NeP (odds ratio 0.52 [0.33-0.71]). Post hoc study associated Chinooks with NeP relief (odds ratio 1.83 [1.17-2.49]). We could not identify relationship between precipitation or humidity with acute NeP exacerbation. However, days with cold temperature ≤ -14°C were associated with greater risk of NeP exacerbation. CONCLUSION: Weather-mediated changes occur for patients with NeP, manifesting as relief from Chinook winds while cold temperature conditions can provoke exacerbations in NeP.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".