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[Review on the evaluation research of the effects of smoke-free legislations on cardiovascular diseases].

2017· review· en· W3024310231 on OpenAlexaboutno aff
Yang Liu, Xia Wan

Bibliographic record

VenuePubMed · 2017
Typereview
Languageen
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsnot available
Fundersnot available
KeywordsCochrane LibraryChinaEnvironmental healthMedicineMeta-analysisMEDLINESmokeWeb of scienceSecondhand smokeIncidence (geometry)BusinessPolitical scienceGeographyInternal medicine

Abstract

fetched live from OpenAlex

A substantial amount of data suggested that exposure to secondhand smoke (SHS) could cause cardiovascular diseases and acute coronary events in nonsmoking adults. In order to protect the public from SHS, more and more countries and regions across the world have enacted and implemented smoke-free legislations. Developed countries, such as USA, Canada, the Great Britain, Ireland, Italy and Spain, have carried out many studies to analyze the effects of smoke-free legislations on the hospital admission, incidence and mortality of cardiovascular diseases with the purpose of confirming the health benefits of the smoking ban and promoting the conduct of the ban. We searched PubMed, EMBASE, Web of Science, Cochrane Library,China National Knowledge Infrastructure(CNKI),WANFANG databaseto summarize the study designs, evaluating indicators, statistical methods and results of these studies to provide reference for evaluating smoke-free legislations in inland cities in China.

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)0.010
metaresearch head score (Gemma)0.038
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.879
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.038
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.436
GPT teacher head0.489
Teacher spread0.053 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations1
Published2017
Admission routes1
Has abstractyes

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