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Record W2983551938 · doi:10.3390/ijerph16224348

Excess Mortality in Istanbul during Extreme Heat Waves between 2013 and 2017

2019· article· en· W2983551938 on OpenAlexaff
Günay Can, Ümit Şahin, Uğurcan Sayılı, Marjolaine Dubé, Beril Kara Esen, Hazal Cansu Acar, Barış İnan, Özden Aksu Sayman, Germain Lebel, Ray Bustinza, Hüseyin Küçükali, Umur Güven, Pierre Gosselin

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsUniversité LavalInstitut National de Santé Publique du Québec
Fundersnot available
KeywordsHeat waveExcess mortalityExtreme heatDemographyEpidemiologyApparent temperatureMortality rateMedicinePublic healthGeographyClimate changeEnvironmental scienceClimatologyEnvironmental healthMeteorologyOceanographySurgeryGeologyInternal medicine

Abstract

fetched live from OpenAlex

Heat waves are one of the most common direct impacts of anthropogenic climate change and excess mortality their most apparent impact. While Turkey has experienced an increase in heat wave episodes between 1971 and 2016, no epidemiological studies have examined their potential impacts on public health so far. In this study excess mortality in Istanbul attributable to extreme heat wave episodes between 2013 and 2017 is presented. Total excess deaths were calculated using mortality rates across different categories, including age, sex, and cause of death. The analysis shows that three extreme heat waves in the summer months of 2015, 2016, and 2017, which covered 14 days in total, significantly increased the mortality rate and caused 419 excess deaths in 23 days of exposure. As climate simulations show that Turkey is one of the most vulnerable countries in the Europe region to the increased intensity of heat waves until the end of the 21st century, further studies about increased mortality and morbidity risks due to heat waves in Istanbul and other cities, as well as intervention studies, are necessary.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.236
GPT teacher head0.433
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations62
Published2019
Admission routes1
Has abstractyes

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