Rapid screening of critically ill patients for low plasma vitamin C concentrations using a point-of-care oxidation–reduction potential measurement
Bibliographic record
Abstract
BACKGROUND: Hypovitaminosis C and vitamin C deficiency are common in critically ill patients and associated with organ dysfunction. Low vitamin C status often goes unnoticed because determination is challenging. The static oxidation reduction potential (sORP) reflects the amount of oxidative stress in the blood and is a potential suitable surrogate marker for vitamin C. sORP can be measured rapidly using the RedoxSYS system, a point-of-care device. This study aims to validate a model that estimates plasma vitamin C concentration and to determine the diagnostic accuracy of sORP to discriminate between decreased and higher plasma vitamin C concentrations. METHODS: Plasma vitamin C concentrations and sORP were measured in a mixed intensive care (IC) population. Our model estimating vitamin C from sORP was validated by assessing its accuracy in two datasets. Receiver operating characteristic (ROC) curves with areas under the curve (AUC) were constructed to show the diagnostic accuracy of sORP to identify and rule out hypovitaminosis C and vitamin C deficiency. Different cut-off values are provided. RESULTS: Plasma vitamin C concentration and sORP were measured in 117 samples in dataset 1 and 43 samples in dataset 2. Bias and precision (SD) were 1.3 ± 10.0 µmol/L and 3.9 ± 10.1 µmol/L in dataset 1 and 2, respectively. In patients with low plasma vitamin C concentrations, bias and precision were - 2.6 ± 5.1 µmol/L and - 1.1 ± 5.4 µmol in dataset 1 (n = 40) and 2 (n = 20), respectively. Optimal sORP cut-off values to differentiate hypovitaminosis C and vitamin C deficiency from higher plasma concentrations were found at 114.6 mV (AUC 0.91) and 124.7 mV (AUC 0.93), respectively. CONCLUSION: sORP accurately estimates low plasma vitamin C concentrations and can be used to screen for hypovitaminosis C and vitamin C deficiency in critically ill patients. A validated model and multiple sORP cut-off values are presented for subgroup analysis in clinical trials or usage in clinical practice.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".