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Record W4309902637 · doi:10.3389/ijph.2022.1605193

Association of Household Utility of Cleaner Fuel With Lower Hypertension Prevalence and Blood Pressure in Chinese Adults

2022· article· en· W4309902637 on OpenAlexafffund
Zhiguang Liu, Mengya Li, Yibing Zhu, Perry Hystad, Yuanting Ma, Sumathy Rangarajan, Qian Zhao, Lihua Hu, Salim Yusuf, Yang Li, Lap Ah Tse

Bibliographic record

VenueInternational Journal of Public Health · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsHamilton Health SciencesMcMaster UniversityPopulation Health Research Institute
FundersNIH Office of the DirectorFaculty of Community and Health Sciences, University of the Western CapeFuwai Hospital, Chinese Academy of Medical SciencesPhilippine Council for Health Research and DevelopmentCanadian Institutes of Health ResearchUnited Nations Relief and Works Agency for Palestine RefugeesNational Institutes of HealthIndependent University, BangladeshServierUniwersytet Medyczny im. Piastów Slaskich we WroclawiuChinese Academy of Medical SciencesUniversiti Kebangsaan MalaysiaAstraZenecaMinistério da Ciência, Tecnologia e InovaçãoAFA FörsäkringVetenskapsrådetMedical Research CouncilIndian Council of Medical ResearchKing Saud UniversityHamilton Health SciencesInternational Development Research CentreSaudi Heart AssociationUniversiti Teknologi MARANational Research FoundationNorth-West UniversityPublic Health Agency of CanadaUniversidad de La FronteraPublic Health AgencyGlaxoSmithKlineSanofiNational Children's Research CentreDepartamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)
KeywordsMedicineBlood pressureLogistic regressionEnvironmental healthOdds ratioEpidemiologyChinaDemographyInternal medicineGeography

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)0.000
metaresearch head score (Gemma)0.001
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 score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.016
GPT teacher head0.234
Teacher spread0.218 · 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

Citations6
Published2022
Admission routes2
Has abstractyes

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