Bacteria and the growing threat of multidrug resistance for invasive cardiac interventions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Invasive cardiovascular procedures which include heart transplantations, congenital heart surgery, coronary artery bypass grafts, cardiac valve repair and replacement, and interventional cardiac electrophysiology procedures represent common mechanisms to treat a variety of cardiovascular diseases across the globe. The majority of these invasive approaches employ antibiotics as a regular and obligatory feature of the invasive procedure. Although the growing incidence of bacterial resistance to currently used antibiotics threatens to curtail the use of all interventional surgical techniques, it remains an underappreciated threat within the arsenal of cardiovascular therapies. It is reasonable to expect that the continued overuse of antibiotics and the frequent management of coronavirus disease 2019 (COVID-19) infected patients with high doses of antibiotics will inevitably accentuate the rise of multidrug resistance. The purpose of this article is to heighten awareness of the role of bacterial infections in cardiovascular disease, the use of antibiotics in today's cardiovascular surgical theaters, the threat facing cardiovascular surgery should multidrug resistance continue to rise unabated, and the development of new antibiotic platforms to solve this problem.
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.
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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it