Structured Allocation of Transcatheter Aortic Valve Replacement Patients during Coronavirus Disease 2019 Pandemic: Impact on Patient Selection and Clinical Results
Bibliographic record
Abstract
Due to shortages of medical resources during the Coronavirus Disease 2019 (COVID-19) pandemic, an allocation algorithm for Transcatheter Aortic Valve Replacement (TAVR) was established. We investigated the impact on patient selection and procedural results. In total, 456 TAVR patients before (pre-COVID-19 group) and 456 TAVR patients after (COVID-19 group) the implementation of our allocation algorithm were compared. Concerning patient characteristics, the COVID-19 group revealed a higher rate of cardiac decompensations/cardiogenic shocks (10.5% vs. 1.3%; p < 0.001), severe angina pectoris (Canadian Cardiovascular Society (CCS) II, III and IV: 18.7% vs. 11.8%; p = 0.004), troponin elevation (>14 ng/L: 84.9% vs. 77%; p = 0.003) and reduced left ventricular ejection fraction (LVEF) (<45%: 18.9% vs. 12%; p = 0.006). Referring to procedural characteristics, more predilatations (46.3% vs. 35.1%; p = 0.001) and a longer procedural time (80.2 min (+/−29.4) vs. 66.9 min (+/−17.5); p < 0.001) were observed. The success rate was evenly high; no differences in safety parameters were reported. Examining the utilization of hospital resources, the COVID-19 group showed a shorter in-hospital stay (8.4 days (+/−5.9) vs. 9.5 days (+/−9.33); p = 0.041) and fewer TAVR patients were treated per month (39 (+/−4.55) vs. 46.11 (+/−7.57); p = 0.03). Our allocation algorithm supported prioritization of sicker patients with similar efficient and safe TAVR procedures. In-hospital stay could be shortened.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".