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Record W2741118346 · doi:10.1186/s12872-017-0650-y

Surface ECG interatrial block-guided treatment for stroke prevention: rationale for an attractive hypothesis

2017· article· en· W2741118346 on OpenAlexaff
Antoni Bayés de Luna, Manuel Martínez‐Sellés, Antoni Bayés‐Genís, Roberto Elosúa, Adrián Baranchuk

Bibliographic record

VenueBMC Cardiovascular Disorders · 2017
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineAtrial fibrillationAngiologyCardiologyStroke (engine)Internal medicineHeart failureDementiaCardiac surgeryDiabetes mellitusDisease

Abstract

fetched live from OpenAlex

Atrial fibrillation (AF) is the most common sustained arrhythmia and is associated with stroke, cognitive impairment, and cardiovascular death. Some predisposing factors − as aging, diabetes, hypertension − induce and maintain electrophysiological and ultrastructural remodeling that usually includes fibrosis. Interatrial conduction disturbances play a crucial role in the initiation of atrial fibrosis and in its associated complications. The diagnosis of interatrial blocks (IABs) is easy to perform using the surface ECG. IAB is classified as partial when the P wave duration is ≥120 ms, and advanced if the P wave also presents a biphasic pattern in II, III and aVF. IAB is very frequent in the elderly and, particularly in the case of the advanced type, is associated with AF, AF recurrences, stroke, and dementia. The anticoagulation in elderly patients at high risk of AF without documented arrhythmias is an open issue but recent data suggest that it might have a role, particularly in elderly patients with structural heart disease, high CHA 2 DS 2 VASc (Congestive heart failure/left ventricular dysfunction, Hypertension, Age ≥ 75 [doubled], Diabetes, Stroke [doubled] – Vascular disease, Age 65–74, and Sex category [female]), and advanced IAB. In this debate, we discuss the association of surface ECG IAB, a marker of atrial fibrosis, with AF and stroke. We also present the rationale that justifies further studies regarding anticoagulation in some of these patients.

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)0.007
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0090.003

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 head0.220
GPT teacher head0.383
Teacher spread0.163 · 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 designTheoretical or conceptual
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

Citations37
Published2017
Admission routes1
Has abstractyes

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