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Record W4386469397 · doi:10.37616/2212-5043.1346

The Prevention and Cardiac Rehabilitation Group of the Saudi Heart Association recommendations regarding establishing a Cardiac Rehabilitation Service

2023· article· en· W4386469397 on OpenAlexaboutno aff
Abdulhalim Jamal Kinsara, Raghdah Aljehani, Jadwiga Wolszakiewicz, Adam Staroń, Muteb Abdullaziz Alsulaimy

Bibliographic record

VenueJournal of the Saudi Heart Association · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRehabilitationMyocardial infarctionPhysical therapyCanadian Cardiovascular SocietySecondary preventionExpert opinionHeart failurePopulationMyocardial revascularizationIntensive care medicineMedical emergencyCardiologyInternal medicineAngina

Abstract

fetched live from OpenAlex

Cardiac rehabilitation (CR) is a cornerstone in the secondary prevention of cardiovascular disease (CVD). Comprehensive cardiac rehabilitation has obtained the highest class of recommendation and the level of evidence for the treatment of patients with ST-segment elevation myocardial infarction, after myocardial revascularization, with chronic coronary syndromes, and in patients with heart failure (HF). Comprehensive cardiac rehabilitation should be implemented as soon as possible, be multi-phasic, and adjusted to the individual needs of the patient. CR is still suboptimally used, and many cardiac centers do not have such services (2). The provision of CR services should be based on standards and key performance indicators, and guidelines containing a minimum standard of cardiac rehabilitation utilization should be published to improve the quality of the CR program. This document presents an expert opinion that summarizes the current medical knowledge concerning the goals, target population, organization, clinical indications, and implementation methods of the CR program in the Kingdom of Saudi Arabia.

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)0.016
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.193
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.013
GPT teacher head0.318
Teacher spread0.305 · 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.

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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

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