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Record W2271085315

Abstract 11299: Economic Impact and Cost-Effectiveness of Cardiac Resynchronization Therapy versus Optimal Medical Therapy in Mild Heart Failure: Long-Term Follow-Up and Projections From REVERSE

2013· article· en· W2271085315 on OpenAlexaboutno aff
Michael R. Gold, Stylianos Tsintzos, Manpreet Sidhu, Stuart Mealing, KerriAnne Fortier, Amie Padhiar, William T. Abraham

Bibliographic record

VenueCirculation · 2013
Typearticle
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCardiac resynchronization therapyHeart failureQRS complexCardiologyInternal medicineEjection fractionRandomized controlled trialCohortProspective cohort studyMedical therapy
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: The REVERSE Study, a prospective double-blinded randomized trial, assessed whether or not Cardiac Resynchronization Therapy (CRT) together with Optimal Medical Therapy (OMT) limited the progression of Heart Failure (HF) compared to OMT alone in Class I/II HF subjects with QRS ≥120 ms and LVEF ≤40%. Patients were implanted with CRT (either with pacing capabilities only “CRT-P”, or combined with a defibrillator “CRT-D”) and randomized to CRT-ON or CRT-OFF. CRT-OFF patients were unblinded after 12 (US/Canada) or 24 months (Europe) and crossed-over to CRT-ON with pre-planned follow-up for 5 years. Our analysis investigates the cost-effectiveness of CRT in this cohort. METHODS: Outcomes were modeled using regression-based techniques informed by actual 5 year data; Cost-Effectiveness for both CRT-ON vs. CRT-OFF and CRT-D vs. CRT-P were assessed. Co-variables used in all analyses were QRS Duration, Ischemic Etiology, LBBB, Gender and Age. NYHA Class based mortality as well as advanced statistical met...

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)0.014
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.005
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.039
GPT teacher head0.335
Teacher spread0.296 · 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 designObservational
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

Citations0
Published2013
Admission routes1
Has abstractyes

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