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Record W4387963372 · doi:10.54097/fbem.v11i3.12944

The Application Progress of Apps in Chronic Disease Health Management

2023· article· en· W4387963372 on OpenAlexaff
Chang Wei

Bibliographic record

VenueFrontiers in Business Economics and Management · 2023
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPopularityChronic diseaseMobile appsFace (sociological concept)DiseasePopulationChinaBusinessDistribution (mathematics)Computer scienceMedicineInternet privacyRisk analysis (engineering)Intensive care medicineEnvironmental healthPolitical scienceWorld Wide WebSociology

Abstract

fetched live from OpenAlex

With the increasingly severe aging of China's population, the incidence of chronic diseases and the difficulty of treatment continue to rise, which provides an excellent environment for the emergence of APP (Application), which is generally praised by society for its convenience, speed, and easy popularity. Moreover, the popularity of mobile medical technology has not only changed the previous face-to-face treatment model but also optimized the distribution of medical resources, thus effectively filling the gaps in the traditional medical model. However, in the field of chronic disease health management, the popularity of APP brings many challenges. Therefore, to facilitate the sustainable and steady development of the mobile medical industry, active measures must be taken to solve these 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.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.690
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.020
GPT teacher head0.347
Teacher spread0.327 · 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.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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