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Record W7151679046

It's not the Future, it's Now: The Involvement of Lithuania and Israel Hospitals in the Innovation Development

2020· article· en· W7151679046 on OpenAlexaboutno aff
Tomas Lapinskas

Bibliographic record

VenueLithuanian University of Health Sciences · 2020
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Healthcare and Medical Tourism
Canadian institutionsnot available
Fundersnot available
KeywordsValue (mathematics)Healthcare systemTechnological changeHealth spendingHealth careHealth policyHealth sector
DOInot available

Abstract

fetched live from OpenAlex

Conflicts of Interest. • I have nothing to declare. The Future of Medicine. 1. Global health systems and bio pharm industry crisis 2. Recent technological breakthroughs - Bio Convergence. National Health Expenditure as a Percentage of GDP 1975 2018. The health expenditure as a percentage of GDP in US, Israel, UK, Switzerland, Canada and Austria has almost doubled , and in some cases, increased even more. Global health expenditure is expected to reach 10 trillion dollars by 2022. Shift from Volume to Value Based Model. Health systems continue to make the transition from Volume Based Model to Value Based Model which is driving the need for technological innovation that can meet the new challenges and needs of the health system. [...].

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)0.009
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.055
Threshold uncertainty score0.183

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0100.004
Open science0.0010.004
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0550.009

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 head0.146
GPT teacher head0.409
Teacher spread0.263 · 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 designQualitative
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

Citations0
Published2020
Admission routes1
Has abstractyes

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