Invasive and noninvasive cardiovascular monitoring options for cardiac surgery
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
Heart disease is the most common cause of death in the United States, with more than 655,000 deaths in the 2018 Centers for Disease Control and Prevention report. Surgical treatment of heart disease is common, expensive, and greater risk than many other surgical endeavors. Cardiovascular monitoring is an important process within this complex clinical setting that requires careful consideration of the patient and the clinical team, as well as the institutional resources to optimize a precise and personalized approach. A comprehensive evaluation of monitoring must consider patient anatomy and cardiopulmonary physiology, hemodynamic and physiologic goals, the phases of care, direct and indirect costs, and operational considerations such as duration of monitoring, therapeutic protocols, and team expertise. Invasive monitoring is a universally accepted component of cardiac surgical perioperative care, but there remain unresolved controversies as to the optimal monitoring strategies to optimize efficacy and efficiency. One of the long-standing controversies is the use of a pulmonary artery catheter (PAC) versus other monitoring alternatives (Figure
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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