Central Venous Oxygen Saturation for Estimating Mixed Venous Oxygen Saturation and Cardiac Index in the ICU: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVES: The objectives of our systematic review and meta-analyses were to determine the diagnostic accuracy of central venous oxygen saturation (Scv o2 ) in estimating mixed venous oxygen saturation (Sv o2 ) and cardiac index in critically ill patients. DATA SOURCES: A systematic search using MEDLINE, Cochrane Central Register of Controlled Trials, and Embase was completed on May 6, 2024. STUDY SELECTION: Studies of patients in the ICU for whom Scv o2 and at least one reference standard test was performed (thermodilution and/or Sv o2 ) were included. DATA EXTRACTION: Individual patient data were used to calculate the pooled intraclass correlation coefficient (ICC) for Sv o2 and Spearman correlation for cardiac index. The Quality Assessment of Diagnostic Accuracy Studies-2 and Grading Recommendations Assessment, Development, and Evaluation tools were used for the risk of bias and certainty of evidence assessments. DATA SYNTHESIS: Of 3427 studies, a total of 18 studies with 1971 patients were identified. We meta-analyzed 16 studies (1335 patients) that used Sv o2 as a reference and three studies (166 patients) that used thermodilution as reference. The ICC for reference Sv o2 was 0.83 (95% CI, 0.75-0.89) with a mean difference of 2.98% toward Scv o2 . The Spearman rank correlation for reference cardiac index is 0.47 (95% CI, 0.46-0.48; p < 0.0001). CONCLUSIONS: There is moderate reliability for Scv o2 in predicting Sv o2 in critical care patients with variability based on sampling site and presence of sepsis. There is limited evidence on the independent use of Scv o2 in predicting cardiac index.
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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 | 0.031 | 0.069 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.054 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".