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Record W4200535331 · doi:10.1002/9781119536475.ch17

Passive Arrhythmias

2021· other· en· W4200535331 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
TopicCardiac pacing and defibrillation studies
Canadian institutionsKingston Health Sciences Centre
Fundersnot available
KeywordsMedicineBradycardiaCardiologyJunctional rhythmSinus bradycardiaHeart rateVagus nerveSinus (botany)Atrioventricular blockInternal medicineVagal toneElectrocardiographyHeart blockSinus rhythmAnesthesiaAutonomic nervous systemAtrial fibrillationBlood pressure

Abstract

fetched live from OpenAlex

This chapter will describe the most important electrocardiogram (ECG) characteristics of the different passive arrhythmias. When the heart rate is slow as a result of depressed sinus automaticity, sinoatrial block, or atrioventricular (AV) block, an AV junction pacemaker at a normal discharge rate may pace the electrical activity of the heart by delivering one or more pacing stimuli. Sinus automaticity depression is manifest by a slow sinus rhythm, which in young athletes or in patients with vagal predominance may even be less than 30bpm. Pacemaker implantation is urgent if the slow heart rate is due to a depressed AV junctional or ventricular rhythm. Sinus bradycardia and different degrees of AV block are present when a predominant vagal overdrive affects both the right vagus nerve and the left vagus nerve. The chapter discusses the key diagnostic criteria of the different AV blocks that may be found in the surface ECG.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.022
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0220.014

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 head0.023
GPT teacher head0.302
Teacher spread0.279 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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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