Bibliographic record
Abstract
Introduction It is estimated that nearly 500,000 Canadians are currently living with heart failure, a disease process associated with considerable morbidity and mortality. Despite significant evidence for effective medical therapies, heart failure remains one of the leading causes of hospitalization in Canada and patients with the disease experience an annual mortality of up to 10%. 1 Approximately one in three patients with systolic heart failure have some degree of intraventricular conduction delay, manifest as increased QRS duration on electrocardiogram (ECG), the most common of which is left bundle branch block (LBBB). This conduction delay, or electrical dyssynchrony, can lead to mechanical uncoupling and inefficiency, which, in turn, can lead to exacerbation of systolic dysfunction, altered myocardial metabolism, functional mitral regurgitation, negative remodeling and worsening clinical outcomes.Cardiac resynchronization therapy (CRT), also known as biventricular pacing, involves coordinating contraction between the left (LV) and right ventricles (RV) through programmed pacing of both ventricles. CRT is an established non-pharmacological therapy for patients with systolic heart failure due to a low ejection fraction, who have a QRS >130 ms and who are symptomatic despite optimal medical therapy. In carefully selected patients, CRT has been shown to promote positive LV remodeling, increase functional capacity, improve quality of life, reduce heart failure hospitalizations and reduce mortality. 2 CRT systems can include defibrillator capabilities (CRT-D) or act as a stand-alone pacemaker (CRT-P). The insertion of a CRT system consumes significant resource (costs), requires a commitment to regular clinical follow-up, and the acceptance of permanent implantation of a large medical device. Clinicians are tasked with identifying patients who would be expected to benefit from CRT and making the decision whether to proceed with CRT implantation. Therefore a careful consideration of the risks and benefits of this technology is required by both the healthcare providers and the patient. Herein we hope to offer guidance on identifying ideal candidates for CRT and to remind health care providers that the patients’ goals must be taken into consideration when counseling a patient for treatment with CRT.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.080 | 0.028 |
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".