The problem of non-response to cardiac resynchronization therapy
Bibliographic record
Abstract
PURPOSE OF REVIEW: Cardiac resynchronization therapy improves quality of life, exercise performance, left ventricular ejection fraction, and reduces heart failure hospitalizations and mortality in patients with New York Heart Association class III or IV congestive heart failure and intraventricular conduction delay. A number of key clinical research questions remain, perhaps most importantly the issue of why apparently suitable patients do not respond to cardiac resynchronization therapy. These issues are also relevant to patients who do respond to cardiac resynchronization therapy as potentially their response might be further increased. This article will review the data regarding the frequency of the problem of non-response to cardiac resynchronization therapy and then discuss the postulated reasons and potential solutions. RECENT FINDINGS: Rates of non-response to cardiac resynchronization therapy are often quoted as 20-30%, but a critical analysis of the data would suggest the true non-responder rate can be estimated as perhaps 40-50%. The data indicate that on a population basis non-response is multi-factorial and the extent of mechanical dyssynchrony, left ventricular pacing site and cause of congestive heart failure are likely to be important. Ongoing research is exploring the utility of various techniques for quantifying mechanical dyssynchrony and the potential benefits of targeted left ventricular lead placement and post-implant optimization. SUMMARY: Cardiac resynchronization therapy is a major breakthrough in treatment for advanced congestive heart failure patients. There is substantial rate of non-response to this therapy, however, and research is exploring various ways to increase the response to the technique.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".