Outcomes in Patients Undergoing Surgical Aortic Valve Replacement With vs Without a Preoperative Heart Team Assessment
Bibliographic record
Abstract
Background This study sought to compare characteristics and outcomes of patients who underwent surgical aortic valve replacement (SAVR) after being referred to a heart team (HT), to those of patients referred directly for SAVR. Methods An analysis of patients who underwent SAVR from 2015 to 2020 was conducted. Patients were categorized into 3 groups, as follows: (i) H-HT: patients referred to the HT from 2015 to 2017 (historical cohort); (ii) C-HT: patients referred to the HT from 2018 to 2020 (contemporary cohort); and (iii) No-HT: patients referred directly to cardiac surgery from 2018 to 2020. Two subanalyses were performed: H-HT vs C-HT patients, and C-HT vs No-HT patients. The primary outcome was a composite of in-hospital mortality, prolonged intubation, reoperation, sternal wound infection, and stroke. Results This study consisted of 288 patients, distributed as follows: H-HT (n = 45); C-HT (n = 51); and No-HT (n = 192). The mean ages of H-HT, C-HT, and No-HT patients was 76.3 ± 6.9 years, 73.3 ± 7.6 years, and 69.6 ± 9.7 years, respectively ( P = 0.0001). H-HT, C-HT, and No-HT patients had average Society of Thoracic Surgeons scores of 4.8 ± 2.2, 3.2 ± 1.6, and 4.2 ± 2 ( P = 0.002), respectively. The composite outcome rate was more than 5 times higher among H-HT patients compared to that among the C-HT patients (20.0 vs 3.9%, P = 0.02), and was numerically higher in No-HT compared to C-HT patients (13.0 vs 3.9%, P = 0.07). Conclusions Referral to an HT appears to be primarily driven by higher chronological age rather than overall risk profile. Patients assessed by the HT prior to undergoing SAVR have a low incidence of complications, comparable to that among patients referred directly to 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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".