Impact of climate and air pollution on acute coronary syndromes: an update from the European Society of Cardiology Congress 2017
Bibliographic record
Abstract
During the recent European Society of Cardiology (ESC) Congress 2017 several papers reported data on air pollution and ambient temperature in relation to myocardial infarction (MI). \n \nEnvironmental stressors have an unquestionable influence on cardiac health. In fact, global climate change may lead to a variety of negative effects on health, including increased risk of cardiovascular diseases. If greenhouse gas emissions continue unabated, the Intergovernmental Panel on Climate Change (IPCC) concludes with a high degree of certainty that, in most places, there will be more hot and fewer cold temperature extremes [1 Collins M, Knutti R, Arblaster J, For the IPCC: long-term climate change: projections, commitments and irreversibility. In: Stocker TF, Qin D, Plattner G-K, et al., editors. Climate Change Report 2013. Cambridge (UK): Cambridge University Press; 2013. Available from: \nhttp://www.climatechange2013.org/ \n [Google Scholar] \n]. Accordingly, it is expected that within a few decades the increase in heat related mortality will outweigh gains due to fewer cold periods [2 Forzieri G, Cescatti A, Batista e Silva F, et al. Increasing risk over time of weather-related hazards to the European population: a data-driven prognostic study. Lancet Planet Health. 2017;1:e200–e208. \n[Crossref], , [Google Scholar] \n], especially in tropical developing countries with limited adaptive capacities and large exposed populations [3 Smith KR, Woodward A, Campbell-Lendrum D, et al. Human health: impacts, adaptation, and co-benefits. In: Field CB, Barros VR, Dokken DJ, et al., editors. Climate Change 2014: impacts, Adaptation, and Vulnerability. Part a: global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge (UK): Cambridge University Press; 2014. p. 709–754. \n [Google Scholar] \n].
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".