Association between frailty and self-reported health following heart valve surgery
Bibliographic record
Abstract
BACKGROUND: Knowledge about the association between frailty and self-reported health among patients undergoing heart valve surgery remains sparse. Thus, the objectives were to I) describe changes in self-reported health at different time points according to frailty status, and to II) investigate the association between frailty status at discharge and poor self-reported health four weeks after discharge among patients undergoing heart valve surgery. METHODS: In a prospective cohort study, consecutive patients undergoing heart valve surgery, including transapical/transaortic valve procedures were included. Frailty was measured using the Fried score, and self-reported health using the Kansas City Cardiomyopathy Questionnaire (KCCQ) and the EuroQoL-5 Dimensions 5-Levels Health Status Questionnaire (EQ-5D-5L).To investigate the association between frailty and self-reported health, multivariable logistic regression models were used. Analyses were adjusted for sex, age, surgical risk evaluation (EuroScore) and procedure and presented as odds ratios (OR) with 95% confidence intervals (CI). RESULTS: Frailty was assessed at discharge in 288 patients (median age 71, 69% men); 51 patients (18%) were frail. In the multivariable analyses, frailty at discharge remained significantly associated with poor self-reported health at four weeks, OR (95% CI): EQ-5D-5L Index 3.38 (1.51-7.52), VAS 2.41 (1.13-5.14), and KCCQ 2.84 (1.35-5.97). CONCLUSION: Frailty is present at discharge in 18% of patients undergoing heart valve surgery, and being frail is associated with poor self-reported health at four weeks of follow-up. This supports a clinical need to address the unique risk of frail patients among heart valve teams broadly, and not only to measure frailty as a marker of operative risk.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| 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".