Assessing the Long-term Outcomes, Quality of Life and cost-effectiveness in Saudi Patients Undergoing Cardiac Surgery for Coronary Artery Disease: A Cross-sectional Study
Bibliographic record
Abstract
Abstract Introduction: Cardiac surgery offers durable clinical benefits in managing coronary artery disease (CAD). However, data from the Saudi context are limited on long-term health impacts, quality of life (QoL) and value. This study aims to address this knowledge gap. Subjects and Methods: A cross-sectional survey involved 1377 Saudi patients who underwent cardiac surgery for CAD. A validated questionnaire assessed demographics, medical history, complications, health status, lifestyle modifications, QoL parameters and healthcare utilisation. Descriptive analyses characterised responses. Results: Most patients underwent coronary artery bypass grafting/percutaneous coronary intervention over 5 years ago, with 85% very satisfied. Half experienced complications, 79% required chronic medications and 34% needed further procedures. Chest pain prevalence exceeded 50%, whereas two-thirds rated cardiovascular health as excellent. QoL improvements entailed reduced angina and better physical/social functioning for many. Outpatient follow-up occurred regularly for 85%. Conclusion: Cardiac surgery conferred durable clinical benefits in alleviating angina and improving functionality. However, ongoing active medical management appeared necessary, given residual symptoms and readmission rates. Future prospective cohorts can confirm long-term costs and savings gained from life expectancies and disability prevention. Locally tailored programmes considering population risks may optimise outcomes further by facilitating lifestyle changes and complication mitigation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".