Bibliographic record
Abstract
PURPOSE OF REVIEW: The surgical management of patients with ischemic cardiomyopathy, especially those with left ventricular ejection fractions less than 20% has historically been controversial. The original trials of surgical revascularization versus medical therapy intentionally excluded those patients with an ejection fraction less than 35%. The significant advances in medical therapy for heart failure over the past 30 years (β-blockade, angiotensin converting enzyme inhibitors, internal cardiac defibrillator and so on) also mandated a reevaluation of the potential benefits of surgical revascularization in this high-risk subset. The purpose of this review is to examine the data from the Surgical Treatment for Ischemic Heart Failure (STICH) trial, initially reported in 2009 and 2011. RECENT FINDINGS: Recent reports published in 2016 have now clearly defined the role of surgery over medical therapy for these patients. Furthermore, although the benefits of surgical ventricular reconstruction were once questioned by the results of STICH, further analyses identify that an appropriately selected patient who undergoes a technically adequate operation will derive benefit from surgical ventricular reconstruction. SUMMARY: The conclusions from the various substudies examining STICH trial data now indicate that in surgical candidates with graftable coronary artery disease, surgical revascularization provides a reduction in overall mortality, cardiovascular deaths and hospitalizations, independent of symptoms, baseline left ventricular size or function and surprisingly, regardless of any viability or evidence of inducible ischemia.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".