'Winter mortality, temperature and influenza' Revisiting Curwen and Devis after a quarter of a century
Bibliographic record
Abstract
Objectives: to update the still quoted 25-year-old finding that a drop in winter temperature by 1 ºC below its mean is associated with 8,000 excess winter deaths in England and Wales.\n\nMethods: time-series regression of excess winter deaths between 1950/1 and 2011/12 on mean outside temperature, influenza and pneumonia deaths and a secular trend.\n\nResults: we find that a 1 ºC decrease in winter temperature below its mean is associated with 5,100 excess winter deaths. Excess winter mortality has been falling on average by approximately 580 deaths per year.\n\nConclusions: Our revised estimates should supersede Curwen and Devis’s in academic and official publications. Notwithstanding there has been some mitigation in the effects of winter temperature on mortality over the last 60 years, excess winter mortality - given its magnitude- should still be a major source of public health concern in England and Wales.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".