Concordance in COVID-19 serology, bone mineralization, and inflammatory analytes between venous and self-collected capillary blood samples exposed to various pre-analytical conditions
Bibliographic record
Abstract
BACKGROUND: The COVID-19 has led to a significant increase in demand for remote blood sampling in clinical trials. This study aims to ascertain the concordance between venous versus capillary samples, processed immediately or exposed to various pre-analytical conditions. METHODS: Participants (≥12 years old) provided a venous blood sample (processed immediately) and capillary samples allocated to one of the following conditions: processed immediately or exposed to 12-, 24-, or 36-h delays at room temperature or 36-h delays with a freeze-thaw cycle. The analytes of interest included SARS-CoV-2 IgG, 25-hydroxy vitamin D (25(OH)D), alkaline phosphate (ALP), calcium (Ca), phosphate (Ph), and c-reactive protein (CRP). Paired samples were considered interchangeable if they met three criteria: minimal within-subject mean difference, 95% of values within desirable total errors, and inter-class correlation (ICC) > 0.90. RESULTS: 90 participants (44.1% male) were enrolled. When comparing rapidly processed venous with capillary samples, 25(OH)D, ALP, and CRP met all three criteria; SARS-CoV-2 IgG met two criteria (mean difference and ICC); and Ca and Ph met one criterion (mean difference). When considering all three criteria, concentrations of 25(OH)D, CRP, and ALP remained unchanged after delays of up to 36 h; SARS-CoV-2 IgG met two criteria (mean difference and ICC); Ca and Ph met one criterion (mean difference). CONCLUSION: These findings suggest that remote blood collection devices can be used to measure anti-SARS-CoV-2 IgG, 25(OH)D, CRP, and ALP. Further analysis is required to evaluate the interchangeability between venous and capillary testing in Ca and Ph levels, which are more sensitive to pre-analytical conditions.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".