Impact of temperature decline from the previous day as a trigger of spontaneous subarachnoid hemorrhage: case-crossover study of prefectural stroke database
Bibliographic record
Abstract
OBJECTIVE: Several environmental factors have been reported to correlate with incidence of spontaneous subarachnoid hemorrhage (SAH). However, because of different patient selection and study designs among these studies, meteorological factors that trigger the incidence of SAH in a short hazard period remain unknown. Among meteorological factors, daily temperature changes may disrupt and violate homeostasis and predispose to cerebrovascular circulatory disturbances and strokes. The authors aimed to investigate whether a decline in the temperature from the highest of the previous day to the lowest of the event day (temperature decline from the previous day [TDP]) triggers SAH in the prefecture-wide stroke database. METHODS: All 28 participating institutions with primary or comprehensive stroke centers located throughout Kochi Prefecture, Japan, were included in the study. Data collected between January 2012 and December 2016 were analyzed, and 715 consecutive SAH patients with a defined date of onset were enrolled. Meteorological data in this period were obtained from the Kochi Local Meteorological Observatory. A case-crossover study was performed to investigate association of TDP and other environmental factors with onset of SAH. RESULTS: The increasing TDP in 1°C on the day of the SAH event was associated with an increased incidence of SAH (OR 1.041, 95% CI 1.007-1.077) after adjustment for other environmental factors. According to the stratified analysis, a significant association between TDP and SAH was observed in women, patients < 65 years old, and patients with weekday onset. Among these factors, increasing TDP had a great impact on SAH onset in patients < 65 years old (p = 0.028, Mann-Whitney U-test). CONCLUSIONS: TDP, temperature decline from the highest of the previous day to the lowest of the day, was correlated with the incidence of spontaneous SAH, particularly in younger patients < 65 years old.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".