Weather and Emergency Room Visits for Migraine Headaches in Ottawa, Canada
Bibliographic record
Abstract
BACKGROUND: Self-reported surveys have indicated that weather can trigger migraine headaches. However, to date, we know of no previous study that has examined the relationship between weather and emergency room (ER) visits for this condition. OBJECTIVE: To examine associations between ER visits for migraines and selected meteorological conditions within the 24 hours preceding the visit. DESIGN AND METHODS: A case-crossover design was used to study 4039 visits for migraines (ICD-9: 346) that occurred at an Ottawa hospital between 1993 and 2000. Meteorological conditions were defined using hourly readings from a fixed-site monitoring station. Conditional logistic regression was used to compare the occurrence of meteorological conditions during the 24 hours leading up to the time of the visit to control periods occurring 1 week before and after. RESULTS: Precipitation-related weather events (fog, snow, rain, thunder) were not associated with migraine visits. Similarly, no associations were observed with changes in atmospheric pressure, wind speed, and relative humidity during the 24 hours preceding presentation. No statistically significant differences in the frequency distribution of clusters defined by relative humidity, atmospheric pressure, and temperature were found between case and control intervals. Conversely, a mean wind speed in excess of 19 km per hour was associated with a reduction in ER visits 8 to 12 hours later. CONCLUSIONS: Our findings provide little support for the hypothesis that ER visits for migraines are related to weather conditions occurring within the 24 hours preceding presentation. These results should be interpreted cautiously as some comparisons are based on a small number of cases, and ER visits for migraine may represent a highly selective group of patients who suffer from this condition.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".