Bibliographic record
Abstract
Although the literature is replete with evidence related to physiological predictors and short-term outcomes of coronary artery bypass graft (CABG) surgery, there is still a paucity of data that encompass a broader perspective of risk and outcomes. The primary objective of this prospective cohort study was to explore the physiological and psychosocial dimensions of preoperative status that may be predictive of the short- and longer term outcomes of CABG surgery. Patients (N = 136) scheduled for elective/urgent CABG surgery were followed from the time of placement on the waiting list until 6 months after the surgery. Significant predictors of intensive care unit length of stay (LOS) included the following: age, urgency of operation, and perioperative complications. Hospital LOS was best predicted by baseline unemployment, longer bypass time, and perioperative complications. Baseline unemployment and less optimism regarding surgery outcomes were predictive of postdischarge home care utilization. Lower baseline physical functioning predicted postdischarge emergency room visits. Sex and baseline mental status predicted quality of life/health satisfaction scores at 6 weeks and 6 months after discharge. The ability to predict patient outcomes has implications for program planning, patient education, and policy development. The findings of this study provide rationale for clinicians, educators, and administrators to consider a broader scope of physiological and psychosocial parameters to predict outcomes of CABG surgery. Although the sample size was relatively small, the broader perspective on risk and outcomes provides insight for strategies to optimize overall outcomes for the CABG surgery population. These findings also establish the cornerstone for ongoing CABG surgery outcomes evaluation and research.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".