Predictors of frailty after cardiovascular surgery and the relationship between frailty and postoperative recovery: A cross‐sectional study
Bibliographic record
Abstract
AIM: To investigate the factors affecting postoperative frailty and the relationship between frailty and postoperative recovery in patients undergoing cardiovascular surgery. DESIGN: The study was descriptive, cross-sectional, and predictive. METHODS: Data were collected by researchers in a university research and application hospital cardiovascular surgery inpatient clinic between March 2022 and March 2023. Sociodemographic-Clinical Characteristics Form, Comorbidity Index, Edmonton Frail Scale, Postoperative Recovery, and Nutritional Risk Screening were used to collect the data. RESULTS: Of the 145 patients included in the study, 65.51% (n = 95) were male and the mean age was 62.02 ± 10.16 years. While frailty was not found to be significant by age group, it was found that women had more comorbidities and were more frail than men. It was found that 17.2% (n = 25) of patients had a history of falls before surgery, 26.2% (n = 38) had a fear of falling after surgery and 17.24% (n = 25) had rehospitalisations. While postoperative recovery index predicted fraility by 34% in patients undergoing cardiovascular surgery; general symptoms and psychological symptoms, which are the sub-dimensions of the postoperative recovery index and comorbidity and, fear of falling after surgery predicted frailty by 61%. The order of importance of variables on fraility: general symptoms (β = 0.297), fear of falling (β = 0.222), psychological symptoms (β = 0.218), Charlson Comorbidity Index (β = 0.183). PATIENT OR PUBLIC CONTRIBUTION: This study clarifies the role of frailty as an important factor influencing the recovery process in patients undergoing cardiovascular surgery. The findings show that frailty has a determining effect on postoperative recovery in these patients. Among the factors affecting frailty status, comorbidities, fear of postoperative falls, and postoperative general and psychological symptoms were found to contribute. These findings emphasise that these factors should be taken into account when assessing and managing the postoperative recovery process. Understanding these factors that influence postoperative frailty is crucial for patient care. Recognising the multifaceted nature of frailty, personalised interventions are needed to improve patient care and postoperative outcomes. Personalised interventions are particularly important for older women with multiple comorbidities, as they are more likely to be frail.
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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 | 0.001 |
| 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) | 0.001 | 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".