Stuck Between a Rock and a Hard Place: The Clinical Conundrum of Managing Cardiac Surgical Patients During the SARS-CoV-2 Pandemic
Bibliographic record
Abstract
Deferring non-emergent cardiac surgery became the strategy of choice for several international healthcare systems afflicted by high case burdens of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2/COVID-19) in order to both conserve valuable healthcare resources and protect patients from possible exposure. Missing from the available dataset to help guide policy development has been a clear understanding of the extent to which COVID-19 infection modulates cardiac surgery outcomes. In their investigation, Bonalumi and colleagues uncovered an inpatient COVID-19 positivity rate of almost 10 times higher than that of the general Italian population, as well as a mortality rate over 20 times higher amongst cardiac surgery patients with perioperative COVID-19 infection compared to those COVID-negative. While the summation of available evidence points to the serious consideration cardiac surgeons must give to delaying surgeries during the COVID-19 pandemic, recognition must be given to the risks that postponing cardiac surgery may have on patient outcomes. Emerging data is beginning to demonstrate the efficacy of vaccination in preventing postoperative COVID-19 infection and morbidity.
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 | 0.009 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".