Cardiac output monitoring – invasive and noninvasive
Bibliographic record
Abstract
PURPOSE OF REVIEW: The purpose of this article is to review various contemporary cardiac output (CO) measurement technologies available and their utility in critically ill patients. RECENT FINDINGS: CO measurement devices can be invasive, minimally invasive, or noninvasive depending upon their method of CO measurement. All devices have pros and cons, with pulmonary artery catheter (PAC) being the gold standard. The invasive techniques are more accurate; however, their invasiveness can cause more complications. The noninvasive devices predict CO via mathematical modeling with several assumptions and are thus prone to errors in clinical situations. Recently, PAC has made a comeback into clinical practice especially in cardiac intensive care units (ICUs). Critical care echocardiography (CCE) is an upcoming tool that not only provides CO but also helps in differential diagnosis. Lack of proper training and nonavailability of equipment are the main hindrances to the wide adoption of CCE. SUMMARY: PAC thermodilution for CO measurement is still gold standard and most suitable in patients with cardiac pathology and with experienced user. CCE offers an alternative to thermodilution and is suitable for all ICUs; however, structural training is required.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".