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Associations Between Extreme Temperatures and Cardiovascular Cause-Specific Mortality: Results From 27 Countries

2022· article· en· W4311154145 on OpenAlexaff
Barrak Alahmad, Haitham Khraishah, Dominic Royé, Ana María Vicedo-Cabrera, Yuming Guo, Stefania Papatheodorou, Souzana Achilleos, Fiorella Acquaotta, Ben Armstrong, Michelle L. Bell, Shih‐Chun Pan, Micheline de Sousa Zanotti Stagliorio Coêlho, Valentina Colistro, Trần Ngọc Đăng, Do Van Dung, Francesca K. de’ Donato, Alireza Entezari, Yue Leon Guo, Masahiro Hashizume, Yasushi Honda, Ene Indermitte, Jouni J. K. Jaakkola, Ho Kim, Éric Lavigne, Whanhee Lee, Shanshan Li, Joana Madureira, Fatemeh Mayvaneh, Hans Orru, Ala Overcenco, Martina S. Ragettli, Niilo Ryti, Paulo Hilário Nascimento Saldiva, Noah Scovronick, Xerxes Seposo, Francesco Sera, Susana Pereira Silva, Massimo Stafoggia, Aurelio Tobı́as, Eric Garshick, Aaron Bernstein, Antonella Zanobetti, Joel Schwartz, Antonio Gasparrini, Petros Koutrakis

Bibliographic record

VenueCirculation · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsOttawa Public HealthUniversity of Ottawa
FundersNational Institute of Environmental Health SciencesNational Center for Advancing Translational SciencesMedical Research CouncilBiocenter, University of OuluUniversidade de Santiago de CompostelaNational Taiwan UniversityHarvard T.H. Chan School of Public HealthUniversidad de la República UruguayNational Taiwan University HospitalHakim Sabzevari UniversityKuwait UniversityAgencia Estatal de InvestigaciónYale UniversitySeoul National UniversityOulun YliopistoUniversitat de ValènciaNational Health and Medical Research CouncilTartu ÜlikoolUniversidade de São PauloFundação de Amparo à Pesquisa do Estado de São PauloMonash UniversityUniversity of BernEuropean CommissionSight Research UKUniversity of NicosiaMedical Research Center OuluEnvironmental Restoration and Conservation AgencyUniversity of CyprusNational Health Research InstitutesNatural Environment Research CouncilU.S. Environmental Protection AgencyCyprus University of TechnologyLondon School of Hygiene and Tropical Medicine
KeywordsPercentileMedicineStroke (engine)Cause of deathHeart failureDemographyExtreme ColdDiseaseMortality rateInternal medicineClimatologyStatistics

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.008
Bibliometrics0.0030.006
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.136
GPT teacher head0.295
Teacher spread0.159 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations398
Published2022
Admission routes1
Has abstractyes

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