Predictors of patient-reported health following cardiac surgery
Bibliographic record
Abstract
BACKGROUND: Patient-reported health status is one of the most important aspects of cardiovascular outcomes. The aim of this study was to assess patient-reported health and its determinants following cardiac surgery. METHODS: Cross-sectional study was performed among 128 patients (mean age: 66.02; 35.9% women). Three months after surgery patients filled in The Short Form 12 Health Survey (SF-12) and Brief-Illness Perception Questionnaire (B-IPQ). Patient-reported health was assessed using SF-12 General Health component. RESULTS: The mean General Health score was 47.34 (SD=20.94). General Health was significantly positively correlated with SF-12 Physical and Mental Component Summary (P<0.01). Significant negative correlations were noted between General Health and European System for Cardiac Operative Risk Factor (EuroSCORE) (P=0.012) and Body Mass Index (BMI) (P=0.026). Higher scores on B-IPQ Consequences, Timeline, Identity, Emotional Response (P<0.01) and Concern (P=0.03) were related to worse General Health. Higher perceived effectiveness of surgery (P<0.01) and Treatment control (P=0.003) were associated with higher General Health score. More negative illness perception was significantly related to lower General Health (P<0.01). No significant associations between General Health and mode and weight of the procedure, myocardial infarction, previous percutaneous coronary intervention, New York Heart Association (NYHA) and Canadian Cardiovascular Society (CCS) class and postsurgical complications were noted. Structural equation modeling (SEM) revealed that illness perception domains, BMI and EuroSCORE are the main determinants of General Health. CONCLUSIONS: Modifiable factors, especially illness perception are important determinants of patient-reported health after cardiac surgery. Evaluation of illness perception seems vital since it may lead to address patients' concerns and improve outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".