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Record W1723310269

Inverse electrocardiographic imaging to assess electrical dyssynchrony in cardiac resynchronization therapy patients

2012· article· en· W1723310269 on OpenAlexaff
Fady Dawoud, David Spragg, Karl H. Schuleri, B. Milan Horáček, Henry R. Halperin, Albert C. Lardo

Bibliographic record

VenueComputing in Cardiology Conference · 2012
Typearticle
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCardiac resynchronization therapyCardiologyInternal medicineVentricular dyssynchronyMedicineElectrocardiographyHeart failureBiomedical engineeringEjection fraction
DOInot available

Abstract

fetched live from OpenAlex

Electrical dyssynchrony is postulated to be one of the main factors contributing to non-response of patients to cardiac resynchronization therapy (CRT). We applied inverse epicardial imaging computed from patient-specific geometry and body-surface potential recordings to assess global and regional electrical dyssynchrony. Patients were imaged pre- and post-device implantation, without and with pacing function (P-OFF and P-ON). The reconstructed maps of activation in the dyssynchronous pre-CRT rhythm agree with published contact mapping activation maps with earliest activation starting on the RV free wall and slowly spreading to the LV in a U-shaped pattern. The new ΔQRSi metric captures global pre-CRT dyssynchrony showing negative values (−0.32±0.10) with minimal variability beats (coefficient of variation 10±3.6%) while post-CRT pacing indicates positive values (0.18±0.11). The imaging method is well-suited to study electrical dyssynchrony and potentially guide CRT lead placement.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0010.000

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 head0.043
GPT teacher head0.315
Teacher spread0.271 · 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 designSimulation or modeling
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

Citations2
Published2012
Admission routes1
Has abstractyes

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