Surgical, Pharmacological, and Patient Factors Affecting Early and or Late Outcomes Following Coronary Artery Bypass Grafting Surgery
Bibliographic record
Abstract
Coronary heart disease continues to be one of the leading causes of death globally. In patients with advanced complex coronary disease, coronary artery bypass grafting surgery (CABG) is considered to be the standard of care. In this thesis, we conducted three studies examining surgical, pharmacological and/or patient factors that can potentially improve patient outcomes after CABG. The first study was an international multi-centre randomize control trial (mRCT) with a 2x2-factorial design (Surgical arm: No-Touch (NT) saphenous vein graft (SVG) harvesting versus conventional (CON) and Pharmacological arm: Fish-oil supplementation versus placebo). We found that NT was not statistically superior to CON for 1-year angiographic and clinical outcomes; however, all major outcomes trended towards favoring the use of the NT technique encouraging longer follow-up and larger studies. Furthermore, 1-year angiographic and clinical outcomes were similar between fish-oils and placebo. The second study was a secondary analysis of another mRCT investigating the use of radial arteries versus SVG in diabetics; we determined that radial artery patency was superior to SVG beyond 5-years after CABG in diabetics and that radials should be used to bypass high grade lesions. Ethnicity is an important patient factor affecting cardiovascular outcomes. The third study was a large propensity matched administrative database study investigating whether South Asians (SA) had poorer outcomes compared to the General Population (GP) after CABG in Ontario; we determined that SA have superior outcomes including event-free late survival compared to GP. These findings, contrary to the previous notion that SA do worse after CABG, will hopefully empower physicians when making recommendations for cardiac procedures. In conclusion, through the above studies, we determined that early patency and clinical outcomes were not statistically different between NT and CON, however, longer and larger studies are warranted. Fish-oils supplementation was not beneficial in improving CABG outcomes. In diabetics, we recommend the use of radial arteries over SVG, especially for high grade stenotic targets. Finally, being a SA seems to be a protective patient factor after CABG and therefore physicians should not be reluctant to recommend CABG to this ethnic group if the decision otherwise is deemed appropriate.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".