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Record W1906089019 · doi:10.1111/aor.12427

Medical Device Market in<scp>C</scp>hina

2015· article· en· W1906089019 on OpenAlexaff
Philip Boyer, Bashir I. Morshed, Tofy Mussivand

Bibliographic record

VenueArtificial Organs · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsUniversity of OttawaCarleton University
Fundersnot available
KeywordsChinaInvestment (military)Domestic marketMedical deviceForeign direct investmentPopulationBusinessPopulation ageingEconomic growthEconomicsInternational tradeMedicinePolitical scienceEnvironmental health

Abstract

fetched live from OpenAlex

With China's growing old-age population and economic presence on the international stage, it has become important to evaluate its domestic and foreign market contribution to medical devices. Medical devices are instruments or apparatuses used in the prevention, rehabilitation, treatment, or knowledge generation with respect to disease or other abnormal conditions. This article provides information drawn from recent publications to describe the current state of the Chinese domestic market for medical devices and to define opportunities for foreign investment potential therein. Recent healthcare reforms implemented to meet rising demand due to an aging and migrating population are having a positive effect on market growth-a global market with a projected growth of 15% per year over the next decade.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.067
GPT teacher head0.267
Teacher spread0.200 · 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

Citations14
Published2015
Admission routes1
Has abstractyes

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