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Cybernetic Approach to Health Assessment

2022· article· en· W4294234317 on OpenAlexaff
E. Yu. Bersenev, Azalia P. Berseneva, A.M. Prysyazhnyuk, Carolyn McGregor, I.А. Berseneva, I.I. Funtova, Аnna G. Chernikova

Bibliographic record

VenueCardiometry · 2022
Typearticle
Languageen
FieldMedicine
TopicSpaceflight effects on biology
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsCyberneticsRelation (database)Process (computing)Computer scienceManagement scienceOperations researchHealth careRisk analysis (engineering)Data scienceSystems engineeringArtificial intelligenceEngineeringMedicineData mining

Abstract

fetched live from OpenAlex

The exploration of orbital space served as a prerequisite for the creation of a new direction of medical science in relation to the very extreme conditions of life of spacecraft crews. Space medicine, relying on the most modern research methods and approaches, thanks to the development of new medical devices and the use of unique data analysis algorithms, has made a significant contribution to the development of telemedicine, medical cybernetics, and prenosological principles for assessing the state of human health. The review reflects the main stages in the development of medical cybernetics and prenosological diagnostics based on the assessment of the regulatory components of the cardiovascular system. Discussed the aspects of the application of the method of mathematical analysis of the heart rhythm in relation to the assessment and forecast of the working capacity of cosmonauts, at the simulating model of microgravity and confinement. Shown the useful methodically apply for the healthcare of manufacture teams at the plants, passenger bus driver’s employments. As the part of appliance of the new advance tools of children and adolescents public health during the educating process at schools. The created system for analyzing the current functional state of human health and mathematical models that make it possible to predict its negative changes make it possible to predetermine the vector of development of medicine in the future. The foundations of knowledge gained over the period of more than 70 years of scientific activity of Professor R.M. Bavsky are reflected in promising areas of cardiology research using computer technologies - such as Cardiometry technologies.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.356
Threshold uncertainty score0.432

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.021
GPT teacher head0.337
Teacher spread0.316 · 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 designNot applicable
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
Published2022
Admission routes1
Has abstractyes

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