Functional Status May Serve as a Predictor of CABG Surgery Outcome in the Elderly Patient
Bibliographic record
Abstract
Despite the growing number of elderly patients undergoing coronary artery bypass graft (CABG) surgery, no study addressing postoperative outcome from the perspective of preoperative functional status has been reported to date. The present investigation therefore undertook to determine, among elderly individuals matched for cardiac status, whether patients with poor functional status have a greater risk of mortality and morbidity following CABG surgery than those with good functional status. Retrospective preoperative and postoperative geriatric functional assessment using a standardized questionnaire was performed on 46 consecutive patients who had undergone CABG in 1994 at age 65 or older. Preoperative functional status was comprised of pre-anginal functional status (before angina limited physical activity) and anginal functional status (during which angina was a noticeable limiting factor). CABG outcome was recorded in terms of postoperative assessment of functional status, morbidity, and mortality. The results of statistical analysis revealed that both pre-anginal and anginal functional status were sensitive predictors of post-operative functional status (p < 0.005 and p < 0.001, respectively). In addition, the presence of comorbidities typically used in the screening of candidates was found to be a sensitive predictor of outcome (p < 0.02). However, the presence of comorbidities was not significantly linked to poor preoperative functional status (p > 0.05), indicating that these two predictors may screen for different elderly sub-populations at high risk for negative outcome of CABG. If confirmed by further studies of elderly patients undergoing CABG, these results suggest a new and important role for functional status as a predictor of CABG outcome in the elderly. Furthermore, these results may be useful toward the development of a reliable tool in screening for high risk of poor outcome among elderly candidates for CABG surgery.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".