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Record W2605642451 · doi:10.1002/ejhf.795

Predictors of Short-Term Clinical Response to Cardiac Resynchronization Therapy

2017· review· en· W2605642451 on OpenAlexaff
Cecilia Linde, William T. Abraham, Michael R. Gold, Jean‐Claude Daubert, Anthony Tang, James B. Young, Lou Sherfesee, J. Harrison Hudnall, Dedra H. Fagan, John G.F. Cleland

Bibliographic record

VenueEuropean Journal of Heart Failure · 2017
Typereview
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsVancouver Island UniversityUniversity of British Columbia
FundersNational Institute for Health and Care Research
KeywordsMedicineEjection fractionQRS complexCardiologyHeart failureInternal medicineCardiac resynchronization therapyOdds ratioRandomized controlled trialClinical trial

Abstract

fetched live from OpenAlex

AIMS: Cardiac resynchronization therapy (CRT) reduces morbidity and mortality in patients with symptomatic heart failure and QRS prolongation but there is uncertainty about which patient characteristics predict short-term clinical response. METHODS AND RESULTS: In an individual patient meta-analysis of three double-blind, randomized trials, clinical composite score (CCS) at 6 months was compared in patients assigned to CRT programmed on or off. Treatment-covariate interactions were assessed to measure likelihood of improved CCS at 6 months. MIRACLE, MIRACLE ICD, and REVERSE trials contributed data for this analysis (n = 1591). Multivariable modelling identified QRS duration and left ventricular ejection fraction (LVEF) as predictors of CRT clinical response (P < 0.05). The odds ratio for a better CCS at 6 months increased by 3.7% for every 1% decrease in LVEF for patients assigned to CRT-on compared to CRT-off, and was greatest when QRS duration was between 160 and 180 ms. CONCLUSIONS: In symptomatic chronic heart failure patients (NYHA class II-IV), longer QRS duration and lower LVEF independently predict early clinical response to CRT.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.572
Threshold uncertainty score0.894

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.134
GPT teacher head0.427
Teacher spread0.293 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations33
Published2017
Admission routes1
Has abstractyes

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