Bibliographic record
Abstract
BACKGROUND: Changes in weather and season have been linked to stroke occurrence. However, the association has been inconsistent across stroke types. Calgary is a city in the Chinook belt and is subject to high variability in weather conditions. METHODS: We obtained hourly weather data over a 5-year period from 1996 to 2000; Chinook events were identified according to the accepted definition. We reviewed administrative data to determine stroke occurrence and defined stroke types to maximize specificity of diagnosis. To examine the hypothesis that weather affected the number of strokes occurring in a given day, we compared average daily stroke occurrence on Chinook days and non-Chinook days; we compared mean daily temperature, relative humidity, barometric pressure, and wind speed by the number of strokes occurring on any given day. RESULTS: Annual variation in stroke frequency was observed. No seasonal, monthly, or weekly variation in overall stroke occurrence or occurrence by type was evident. No relationship with changes in weather parameters was observed. CONCLUSIONS: We found no association between weather changes and stroke occurrence. A cause-and-effect relationship between weather and stroke occurrence is dubious because of a lack of consistency across studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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; both teacher heads agree on what is shown here.
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".