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Record W4387377188 · doi:10.59934/jaiea.v3i1.258

Diagnosis Of Cholesterol Disease In Adolescence Using Certainty Factor Method

2023· article· en· W4387377188 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

VenueJournal of Artificial Intelligence and Engineering Applications (JAIEA) · 2023
Typearticle
Languageen
FieldComputer Science
TopicEdcuational Technology Systems
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsCertaintyCholesterolDiseaseMedicineExpert systemPsychologyComputer scienceArtificial intelligenceEndocrinologyPathologyMathematics

Abstract

fetched live from OpenAlex

Expert system is a computer program designed to emulate the abilities of an expert in solving problems in a particular field. This system can assist in making a decision and provide a solution based on the data that has been obtained. Delia General Hospital (RSU) is a hospital in Langkat district that provides health services to the community to cure diseases, one of which is cholesterol. Cholesterol is a fatty substance contained in the blood and produced by the body. Cholesterol has an important role in helping to produce hormones and form cells. However, if the level of cholesterol in the blood is too high, it can cause health problems such as atherosclerosis, stroke, and heart disease. Increased cholesterol levels occur in adolescents due to unhealthy lifestyles and consuming foods high in fat. Certainty factor is a method in artificial intelligence that is used to determine whether a fact is certain or uncertain and provides accurate results by calculating the weight of symptoms determined by an expert and can produce answers to uncertain questions.

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: none
Teacher disagreement score0.721
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.067
GPT teacher head0.332
Teacher spread0.265 · 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