湲됱꽦 �떖洹� 寃쎌깋 �썑 �삊�떖利� �솚�옄�뿉�꽌�쓽 愿��긽�룞留� �슦�쉶�닠 �썑 醫뚯떖�떎 �닔異� 湲곕뒫�쓽 �샇�쟾
Bibliographic record
Abstract
Background: Acute myocardial infarction (MI) is a life-threatening disease and surgical revascularization plays a major role in selected cases. The purpose of this study is to evaluate the left ventricular contractility improvement by examining the wall motion score index (WMSI) and left ventricular ejection fraction (LVEF) in patients who underwent surgical revascularization under diagnosis of acute MI. \n\nMaterial and Method: From January, 2001 to December, 2004, 149 patients who underwent coronary artery bypass surgery within 2 weeks of acute MI were included. We evaluated pre- and postoperative left ventricular contractility by measuring WMSI and LVEF and examined the associating factors. \n\nResult: WMSI decreased from 1.54짹4.30 to 1.43짹0.40 (p竊�0.001) and LVEF increased from 48.1짹12.2% to 49.7짹12.3% after surgery (p=0.009). Off-pump technique, non-Q wave, anterior MI, and surgery within 7 days after MI were favorable factors for LVEF improvement (p=0.046, p=0.006, p=0.003, p= 0.005, respectively). Conversely, aforementioned factors were irrelevant with WMSI improvement. For triple vessel disease, complete revascularization was favorable factor for WMSI improvement (p竊�0.001). \n\nConclusion: Coronary artery bypass surgery can improve WMSI and LVEF in patients with acute MI. In case of anterior MI with non-Q wave, early surgical revascularization within 7 days may be most beneficial in LVEF improvement. Regarding WMSI, complete revascularization may be essential.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".