Cardiac Output Measurement and Reporting: More Than Just Going with the Flow
Bibliographic record
Abstract
Few clinicians would challenge the notion that maintenance of an adequate cardiac output to support end-organ perfusion is a critical component of safe perioperative care.Yet, the multiplicity of technologies promoted to measure cardiac output, how to judge the quality of such approaches, and how far to extend evidence derived from one measurement modality to another greatly complicate generalization of research to the bedside.These issues underpin the challenges and shortcomings of efforts to determine the clinical effectiveness of goaldirected hemodynamic therapies commonly involving optimizing cardiac output. 1 As a result, inconsistencies in cardiac output measurement methods and reporting pose serious challenges to the quality of health research.In this issue of the Journal, Saugel and colleagues address these escalating challenges of rigor and reproducibility by comparing varied methods of cardiac output monitoring and reporting of research results and proposing a new framework for reporting of such results.2 Developed through a consensus process drawing from an expert group of anesthesiologists, critical care physicians, and biostatisticians, the statistiCal analysis and repOrting of cardiac output Method comPARison studiEs (COMPARE) statement lays welcome groundwork for understanding the degree to which findings from a particular study using a given cardiac output monitoring method may be comparable to another study reporting on a different method.Key components include adopting elements from evolving reporting statements aimed at enhancing the quality and transparency of health research, 3,4 as well as nuances specific to cardiac output monitoring that have frequently been under-reported or absent in earlier studies.Such nuances include the working principles and technical details of the cardiac output measurement method as well as the timing (and, importantly, trending, the
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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 | 0.032 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".