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Record W7083300163 · doi:10.58526/jsret.v1i1.5

Physical Activity on Coronary Heart Disease Patients

2022· article· en· W7083300163 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 Scientific Research Education and Technology (JSRET) · 2022
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsThe Journal of Student Science and Technology
Fundersnot available
KeywordsCoronary heart diseaseOverweightPhysical activityObesityPopulationFramingham Risk ScorePhysical exerciseCoronary disease

Abstract

fetched live from OpenAlex

Lack of physical activity is associated with weight gain, being overweight and obesity which are the main factors causing modern diseases such as coronary heart disease. It is estimated that during the last 15 years, 8.7 million of the world's population have died from coronary heart disease, an increase of 12.2% from 2000. Regular physical activity can reduce the risk of morbidity and mortality of all risks of cardiovascular disease including coronary heart disease. This study aims to describe physical activity in patients with coronary heart disease. The research design used in this study is descriptive with a retrospective approach. The population in this study were all patients with coronary heart disease at Cardiology Departement in Hospital Dr. Wahidin Sudiro Husodo for the period July 2018. The sample size was 102 respondents using Consecutive Sampling techniques. This research was conducted on 2-14 July 2018. The results of this study indicate that almost half of respondents with low intensity of physical activity were 50 people (49.0%)

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)0.002
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
Threshold uncertainty score0.755

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.037
GPT teacher head0.346
Teacher spread0.309 · 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