Value of combined lactate and central venous oxygen saturation measurement in patients with sepsis: a retrospective cohort study
Bibliographic record
Abstract
Introduction. Lactate and central venous oxygen saturation (ScvO2) reflect tissue hypoperfusion but each measure is confounded by many additional factors. These confounding factors differ between lactate and ScvO2. Objectives. We postulated that combined assessment of lactate and ScvO2 may yield information beyond that of each measure alone. Specifically we sought to determine whether lactate has different characteristics and predictive value at different levels of ScvO2. Material and methods. We conducted a retrospective analysis of a Derivation cohort and a Validation Cohort of sepsis patients with lactate and ScvO2 measured within the first 4 hours of intensive care unit admission and 12 hours after resuscitation. Patients were grouped according to: 1) ScvO2 < 60 %; 2) 60 % ≤ ScvO2 < 80 %; 3) ScvO2 ≥ 80 %. Results. Lactate was negatively correlated with ScvO2 in the ScvO2 < 60 % group in both cohorts but was not correlated with ScvO2 in the other ScvO2 groups. Using receiver operator characteristic analysis in the Derivation Cohort, in the ScvO2 ≥ 80 % group lactate was predictive of 28-day mortality with an area under the ROC curve (AUC) of 0.94 and an optimal threshold lactate of 3.0 mmol/L. Using this threshold in the ScvO2 ≥ 80 % groups, 28-day mortality was 32.7 %. Conclusions. Lactate has different characteristics and predictive value at different levels of ScvO2. When ScvO2 < 60 % correlation between lactate and ScvO2 is consistent with a degree of oxygen supply limitation. When ScvO2 ≥ 80 % lactate > 3.0 mmol/L is predictive of mortality.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".