The effect of frailty on postoperative recovery in patients with cardiovascular surgery
Bibliographic record
Abstract
This study aimed to examine the impact of frailty on postoperative morbidity and mortality in patients undergoing cardiovascular surgery, questioning the adequacy of the preoperative American Society of Anesthesiologists (ASA) as the sole assessment tool. In a cohort of 76 patients undergoing cardiovascular interventions, we analyzed demographic data, Edmonton Frail Scale (EFS), ASA scores, Charlson Comorbidity Index values, surgery and hospitalization durations, intraoperative blood pressures, inotropic needs, erythrocyte transfusions, and pre/postoperative hemoglobin levels. Pearson chi-squared and Spearman tests were performed. Correlation of postoperative intensive care unit (ICU) stay, extubation time, ward stay, discharge status, morbidity rates, and ASA and EuroSCORE II results with EFS scores. The demographic profile indicated a mean age of 59.67 ± 13.02 years, with a majority of male patients (59.2%). Frailty status varied, with 48.7% non-frail, 26.3% vulnerable, 18.4% mildly frail, and 6.6% moderately frail. Surgical data revealed an average duration of 300.93 minutes and a mean ICU stay of 54.48 ± 101.16 hours. Statistical analysis showed significant differences in frailty levels based on initial morbidity (χ2 = 10.612, P = .014) but not in ASA score distribution by morbidity status (χ2 = 1.634, P = .442). A negative correlation was observed between EFS scores and hemoglobin levels, along with a positive correlation between the EuroSCORE II score and the duration of intubation, extubation, and ICU stay. Frailty significantly contributes to increased morbidity and necessitates evaluation alongside preoperative ASA scores to inform the need for prehabilitation. The ultimate goal extends beyond patient survival, aiming to ensure recovery while maintaining the quality of life and functional independence.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".