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Record W1498570828 · doi:10.5772/54635

Self-Management Training for Chronic Stable Angina: Theory, Process, and Outcomes

2013· book-chapter· en· W1498570828 on OpenAlexaboutno aff
Michael McGillion, Sheila O’Keefe-McCarthy, Sandra Carroll

Bibliographic record

VenueInTech eBooks · 2013
Typebook-chapter
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsProcess (computing)Training (meteorology)MedicinePsychologyComputer scienceGeography

Abstract

fetched live from OpenAlex

Chronic stable angina (CSA) is a cardinal symptom of coronary artery disease (CAD), charac‐ terized by pain or discomfort in the precordium, shoulder, back, arm, or jaw [1]. Angina pain symptoms—or equivalents such as shortness of breath, fatigue, and nausea—are considered stable if they are experienced over several weeks in the absence of major deterioration [1-3]. Those affected by CSA typically have CAD involving one or more large epicardial arteries, although individuals diagnosed with hypertrophic cardiomyopathy, hypertension, endothe‐ lial dysfunction, or valvular stenosis/deficiencies may also exhibit angina [1]. Symptoms usually occur predictably upon physical exertion and are relieved by rest or nitroglycerin [1]. The severity of symptoms experienced can vary, typically ranging from Canadian Cardiovas‐ cular Society [CCS] class I to class III angina. A number of factors can also aggravate symptoms including heightened emotional states, diet, smoking, and weather [1,4].

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.957
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.026
GPT teacher head0.320
Teacher spread0.294 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2013
Admission routes1
Has abstractyes

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