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Record W2568404092 · doi:10.1016/j.hrcr.2016.10.007

Hierarchical analysis of electrograms to guide termination of persistent atrial fibrillation

2017· article· en· W2568404092 on OpenAlexaff
Andreu Porta‐Sánchez, Andrew C.T. Ha, Sachin Nayyar, Rupin Dalvi, Vijay S. Chauhan

Bibliographic record

VenueHeartRhythm Case Reports · 2017
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsUniversity Health Network
FundersJanssen Pharmaceuticals
KeywordsMedicineAtrial fibrillationCardiologyInternal medicineAtrial tachycardiaCatheter ablation

Abstract

fetched live from OpenAlex

The mechanism(s) perpetuating atrial fibrillation (AF) is poorly understood, and long-term success with catheter ablation remains suboptimal, especially in persistent AF. Ablation strategies beyond pulmonary vein (PV) isolation (PVI) have not been shown to provide incremental benefit in terms of maintaining sinus rhythm.1 However, there are data suggesting that acute termination of AF during ablation of extra-PV sources could lead to improved freedom from AF recurrence.2 As such, there is intense interest to better characterize and identify such potential “drivers” of AF, which may be amenable to ablation and improve outcomes.

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)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.052
GPT teacher head0.375
Teacher spread0.323 · 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 designCase report
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

Citations1
Published2017
Admission routes1
Has abstractyes

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