A simple method for plasma total vitamin C analysis suitable for routine clinical laboratory use
Bibliographic record
Abstract
BACKGROUND: In-hospital hypovitaminosis C is highly prevalent but almost completely unrecognized. Medical awareness of this potentially important disorder is hindered by the inability of most hospital laboratories to determine plasma vitamin C concentrations. The availability of a simple, reliable method for analyzing plasma vitamin C could increase opportunities for routine plasma vitamin C analysis in clinical medicine. METHODS: Plasma vitamin C can be analyzed by high performance liquid chromatography (HPLC) with electrochemical (EC) or ultraviolet (UV) light detection. We modified existing UV-HPLC methods for plasma total vitamin C analysis (the sum of ascorbic and dehydroascorbic acid) to develop a simple, constant-low-pH sample reduction procedure followed by isocratic reverse-phase HPLC separation using a purely aqueous low-pH non-buffered mobile phase. Although EC-HPLC is widely recommended over UV-HPLC for plasma total vitamin C analysis, the two methods have never been directly compared. We formally compared the simplified UV-HPLC method with EC-HPLC in 80 consecutive clinical samples. RESULTS: The simplified UV-HPLC method was less expensive, easier to set up, required fewer reagents and no pH adjustments, and demonstrated greater sample stability than many existing methods for plasma vitamin C analysis. When compared with the gold-standard EC-HPLC method in 80 consecutive clinical samples exhibiting a wide range of plasma vitamin C concentrations, it performed equivalently. CONCLUSION: The easy set up, simplicity and sensitivity of the plasma vitamin C analysis method described here could make it practical in a normally equipped hospital laboratory. Unlike any prior UV-HPLC method for plasma total vitamin C analysis, it was rigorously compared with the gold-standard EC-HPLC method and performed equivalently. Adoption of this method could increase the availability of plasma vitamin C analysis in clinical medicine.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".