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Record W4205660793 · doi:10.1002/9781119536475.ch22

The ECG in Other Heart Diseases

2021· other· en· W4205660793 on OpenAlexaff
Antoni Bayés de Luna, Miquel Fiol, Antoni Bayés‐Genís, Adrián Baranchuk, Roberto Elosúa, Manuel Martínez‐Sellés

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMedicine
TopicPericarditis and Cardiac Tamponade
Canadian institutionsKingston Health Sciences Centre
Fundersnot available
KeywordsMedicineCardiologyInternal medicineAtrial fibrillationHeart diseasevalvular heart diseasePulmonary heart diseaseLeft atrial enlargementMitral valve prolapseRheumatic feverElectrocardiographyPulmonary hypertensionMitral valve

Abstract

fetched live from OpenAlex

This chapter deals with the most important electrocardiographic abnormalities present in heart diseases other than ischemic heart disease and inherited heart diseases. The incidence of valvular heart diseases of rheumatic origin in developed countries is very rare and usually only found in immigrant populations. The chapter explains the most frequent electrocardiogram (ECG) findings and their clinical significance. In mitral valve prolapse, repolarization abnormalities are frequently found in II, III, aVF, and left precordial leads. Isolated aortic valve disease, except at advanced stages, is not typically accompanied by significant signs of left atrial enlargement or atrial fibrillation. Therefore, if these ECG signs are present in non-advanced cases, associated mitral valve disease must be suspected. Rheumatic fever is a systemic inflammatory disease whose target organ is the heart. The chapter deals with acute cor pulmonale, chronic cor pulmonale, and cases of primary pulmonary hypertension. The imaging techniques are more effective than ECG for diagnosing congenital heart diseases.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.024
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0130.000

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 head0.008
GPT teacher head0.275
Teacher spread0.267 · 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
Published2021
Admission routes1
Has abstractyes

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