MétaCan
Menu
Back to cohort
Record W4205724321 · doi:10.1002/9781119536475.ch23

The ECG in Other Diseases and Different Situations

2021· other· en· W4205724321 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
TopicCardiovascular Effects of Exercise
Canadian institutionsKingston Health Sciences Centre
Fundersnot available
KeywordsMedicineDecompensationCardiologyInternal medicinePulmonary embolismPulmonary diseaseEtiologyRepolarizationElectrocardiographyCardiac decompensationSubarachnoid hemorrhageT wavePulmonary heart diseaseHeart failure

Abstract

fetched live from OpenAlex

This chapter reviews the different electrocardiogram (ECG) characteristics in non-cardiac processes and other situations, which are sometimes striking. Cerebrovascular accidents, and particularly subarachnoid hemorrhage, frequently show general repolarization abnormalities of the T wave, which can be highly negative or highly positive but are generally wide, and with long QT and mirror patterns in frontal plane leads. Numerous lung diseases may cause involvement of the right chambers both: in the acute setting, acute cor pulmonale due to pulmonary embolism or acute decompensation of chronic obstructive pulmonary disease; and in the chronic phase, emphysema and chronic cor pulmonale. The chapter comments on the global ECG changes that may be found in athletes in surface ECG, divided into frequent ECG findings that may be training related and uncommon ECG findings that are not training related. Many drugs alter the ECG morphology. Alcoholics with heart failure exhibit typical alterations frequently found in dilated cardiomyopathies of other etiologies.

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 score0.122
Threshold uncertainty score0.999

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)0.0020.000

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 head0.007
GPT teacher head0.257
Teacher spread0.249 · 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

Explore more

Same topicCardiovascular Effects of ExerciseFrench-language works237,207