The Impact of Frailty on Functional Survival in Patients 1-Year Post-Cardiac Surgery
Bibliographic record
Abstract
Frailty is an emerging concept in medicine yet to be adequately explored as a risk factor in cardiac surgery. Frailty is a geriatric syndrome of decreased physiologic reserves and increased vulnerability to stressors. It may be a strong predictor of adverse events following cardiac surgery such as post-operative delirium. Given that elderly patients are increasingly referred for cardiac surgery, the prevalence of frailty amongst this group is on the rise. Risk prediction, not just for mortality but also morbidity is pivotal in order to determine the optimal timing and selection for this increasingly complex group of patients. However, currently available risk scores (i.e. Euroscore II, Society of Thoracic Surgery) fail to account for the patient’s total physiologic reserves that will be called upon at the time of surgery. We have previously identified that, when using detailed frailty assessment tools, ~55% of elective cardiac surgery patients in Manitoba can be deemed frail. Preoperative frailty was associated with a 5-8-fold increase in the occurrence of postoperative delirium and prolonged hospital length of stay. While these represent important and novel findings, there is a pressing need to understand the longer-term impact of frailty before it can be integrated into current cardiac surgery risk scores. The study objective, therefore is to examine the mid- and long-term impact of frailty, with and without the co-occurrence of delirium, on outcomes following cardiac surgery. We ultimately aim to understand how incorporation of frailty assessment impact patient well-being in elderly patients undergoing cardiac 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".