Cardiac output monitoring: Technology and choice
Bibliographic record
Abstract
The accurate quantification of cardiac output (CO) is given vital importance in modern medical practice, especially in high-risk surgical and critically ill patients. CO monitoring together with perioperative protocols to guide intravenous fluid therapy and inotropic support with the aim of improving CO and oxygen delivery has shown to improve perioperative outcomes in high-risk surgical patients. Understanding of the underlying principles of CO measuring devices helps in knowing the limitations of their use and allows more effective and safer utilization. At present, no single CO monitoring device can meet all the clinical requirements considering the limitations of diverse CO monitoring techniques. The evidence for the minimally invasive CO monitoring is conflicting; however, different CO monitoring devices may be used during the clinical course of patients as an integrated approach based on their invasiveness and the need for additional hemodynamic data. These devices add numerical trend information for anesthesiologists and intensivists to use in determining the most appropriate management of their patients and at present, do not completely prohibit but do increasingly limit the use of the pulmonary artery catheter.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".