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Record W2139662895 · doi:10.1109/memb.2008.923955

Perspectives on High Technologies for Low-Cost Healthcare

2008· article· en· W2139662895 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Engineering in Medicine and Biology Magazine · 2008
Typearticle
Languageen
FieldEngineering
TopicWireless Body Area Networks
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHealth careWearable computerService (business)BusinessChinaKey (lock)ArchitectureTelemedicineHealthcare systemRisk analysis (engineering)Computer scienceComputer securityMarketingEconomic growthGeographyEconomics

Abstract

fetched live from OpenAlex

This article discusses some of the unique demographic and epidemiological changes that China faces. As China is still a developing country, most of its people cannot afford expensive healthcare solutions. Especially in the poor rural areas, healthcare service is a luxury for some people. Therefore, this article summarizes several key strategies to reduce medical expenditures at the national level and proposes to develop a new information system in the form of a personal, home, community, and hospital (PHCH) four-layered architecture. Using the management of blood pressure (BP) as an example, we have shown that innovative technologies in wearable medical devices and body area networks (BANs) can be developed to collect information for this new system to overcome geographic and financial constraints and to provide a low-cost and effective solution to manage chronic health problems.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.769

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.024
GPT teacher head0.260
Teacher spread0.236 · 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