Closing the care gap: combining enhanced recovery with minimally invasive valve surgery
Bibliographic record
Abstract
PURPOSE OF REVIEW: Patients with advanced age and frailty require interventions for structural heart disease at an increasing rate. These patients typically experience higher rates of postoperative morbidity, mortality and prolonged hospital length of stay, loss of independence as well as associated increased costs to the healthcare system. Therefore, it is becoming critically important to raise awareness and develop strategies to improve clinical outcomes in the contemporary, high-risk patient population undergoing cardiacprocedures. RECENT FINDINGS: Percutaneous options for structural heart disease have dramatically improved the therapeutic options for some older, frail, high-risk patients; however, others may still require cardiac surgery. Minimally invasive techniques can reduce some of the physiologic burden experienced by patients undergoing surgery and improve recovery. Enhanced Recovery After Cardiac Surgery (ERAS Cardiac) is a comprehensive, interdisciplinary, evidence-based approach to perioperative care. It has been shown to improve recovery and patient satisfaction while reducing complications and length of stay. SUMMARY: Combining minimally invasive cardiac surgery with enhanced recovery protocols may result in improved patient outcomes for a patient population at high risk of morbidity and mortality following cardiac surgery.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".