Seasonality of mortality: the September phenomenon in Mediterranean countries
Bibliographic record
Abstract
BACKGROUND: Seasonal increases in the mortality rate have been associated with excessively cold or hot weather. We evaluated monthly patterns of mortality in selected countries. METHODS: We analyzed all-cause mortality statistics from 5 European Mediterranean countries (Cyprus, France, Greece, Italy, Spain), Sweden, North America (United States and Canada), Australia, New Zealand and Japan. We extracted and tabulated data on monthly all-cause mortality in the general population from the earliest to the latest year that records were available. RESULTS: We identified relevant data for a period of 2-57 years in each country. In the Mediterranean countries, the lowest average daily mortality was observed in September (all countries, 125/168 [74%] years). The fewest deaths were in August in Sweden (14/20 [70%] years) and North America (32/50 [64%] years). The fewest deaths in Japan occurred in July (2/2 [100%] years). In the southern hemisphere, the lowest mortality in Australia occurred in March (7/10 [70%] years) and in February for New Zealand (cumulative over 24 years). INTERPRETATION: Mortality in the general population declines in the late summer to early fall months in the countries evaluated. Environmental parameters may partly account for these associations, and further research is needed on the contribution of additional factors such as summer vacations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".