Rationale and Design of the Adaptresponse Trial: A Prospective Randomized Study of Cardiac Resynchronization Therapy with Preferential Adaptive Left Ventricular-Only Pacing
Bibliographic record
Abstract
algorithm reduces the incidence of the combined endpoint of all-cause mortality and intervention for heart failure (HF) decompensation, compared with conventional cardiac resynchronization therapy (CRT), among patients with a CRT indication, left bundle branch block (LBBB) and normal atrioventricular (AV) conduction. The AdaptResponse study is a prospective, randomized, controlled, single-blinded, multicentre, clinical trial (ClinicalTrials.gov Identifier: NCT02205359), conducted at up to 200 centres worldwide. Following enrolment and baseline assessment, eligible subjects will be implanted with a CRT system containing the AdaptivCRT algorithm, and randomized in a 1:1 fashion to either a treatment ('AdaptivCRT') or control ('Conventional CRT') group. The study is designed to observe a primary endpoint in 1100 patients ('event-driven') and approximately 3000 patients will be randomized. The primary endpoint is the composite of all-cause mortality and intervention for HF decompensation; secondary endpoints include all-cause mortality, intervention for HF decompensation, clinical composite score (CCS) at 6 months, atrial fibrillation, quality of life measured by the Kansas City Cardiomyopathy Questionnaire (KCCQ), health outcome measured by the EQ-5D instrument, all-cause readmission after a HF admission, and cost-effectiveness. The AdaptResponse clinical trial is powered to assess clinical endpoints and is expected to provide definitive evidence on the incremental utility of AdaptivCRT-enhanced CRT systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.053 | 0.038 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".