MétaCan
Menu
Back to cohort
Record W2054276704 · doi:10.1586/14737167.8.4.349

Economic burden of cardiovascular diseases in China

2008· article· en· W2054276704 on OpenAlexaff
Li Yang, Ming Wu, Bin Cui, Judy Xu

Bibliographic record

VenueExpert Review of Pharmacoeconomics & Outcomes Research · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsNew York Institute of Technology
Fundersnot available
KeywordsChinaMedical insuranceEnvironmental healthInvestment (military)Economic costIndirect costsMedicineRural areaMedical costsPopulationDiseaseEconomic growthDemographyBusinessSocioeconomicsHealth careGeographyEconomicsActuarial sciencePolitical science

Abstract

fetched live from OpenAlex

Cardiovascular diseases (CVD) have become the principle cause of death and disability among the middle-aged and elderly both in urban and rural areas of China. The objective of this study is to estimate the direct costs of CVD in China. Direct costs were estimated for the Chinese population with CVD in 2003 by sex, age, geography, type of medical condition and medical insurance, and then calculated based on the 2003 National Health Services Survey. The annual average direct medical cost and direct nonmedical cost were 4238.3 Yuan (US$529.8) and 153.9 Yuan (US$19.2) in urban areas, 2302.5 Yuan (US$278.1) and 416.4 Yuan (US$50.3) in rural areas, respectively. On average, only 23.9% of outpatient costs and 35.2% in-patient costs could be paid by various kinds of medical insurance. This disease burden led to 209.0 billion Yuan (US$26.1 billion) in direct costs in 2003. The strong positive association between CVD, and the economic burden to families and society, demonstrates the need for greater investment to prevent CVD 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 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)0.001
metaresearch head score (Gemma)0.002
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 score0.080
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.058
GPT teacher head0.453
Teacher spread0.394 · 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

Citations26
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueExpert Review of Pharmacoeconomics & Outcomes ResearchSame topicHealthcare Systems and ReformsFrench-language works237,207