MétaCan
Menu
Back to cohort
Record W2766777687 · doi:10.1016/j.hrcr.2017.10.007

Unconventional warfare: Successful ablation of ventricular tachycardia by direct ventricular puncture in a patient with double mechanical heart valves

2017· article· en· W2766777687 on OpenAlexaff
Syamkumar Divakara Menon, Richard Whitlock, Nicholas Valettas, Jeff S. Healey

Bibliographic record

VenueHeartRhythm Case Reports · 2017
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrhythmias and Treatments
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineAblationCatheter ablationVentricular tachycardiaCardiologyTachycardiaInternal medicineCatheterReentrySurgery

Abstract

fetched live from OpenAlex

Key Teaching Points•Catheter ablation of ventricular tachycardia (VT) can improve morbidity and mortality in patients with structural heart disease with implantable cardioverter-defibrillators implanted for primary as well as secondary prevention.•Substrate-based ablation is a safe and effective strategy for reentrant VTs, which is the predominant mechanism of tachycardia in these patients.•Access to the “substrate” will be challenging in some cases, especially in the presence of mechanical prosthetic heart valves.•Unconventional approaches are needed in those cases where a “hybrid” approach of catheter-based ablation and surgical ablation is useful. •Catheter ablation of ventricular tachycardia (VT) can improve morbidity and mortality in patients with structural heart disease with implantable cardioverter-defibrillators implanted for primary as well as secondary prevention.•Substrate-based ablation is a safe and effective strategy for reentrant VTs, which is the predominant mechanism of tachycardia in these patients.•Access to the “substrate” will be challenging in some cases, especially in the presence of mechanical prosthetic heart valves.•Unconventional approaches are needed in those cases where a “hybrid” approach of catheter-based ablation and surgical ablation is useful.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.830

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.010
GPT teacher head0.266
Teacher spread0.256 · 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.

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

Citations7
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueHeartRhythm Case ReportsSame topicCardiac Arrhythmias and TreatmentsFrench-language works237,207