Comparison and Agreement between Venous and Capillary Blood for the Analysis of Trace Elements
Bibliographic record
Abstract
The use of capillary blood has many advantages over traditional venipuncture, and it has been proposed as an alternative to the use of venous blood in many fields. Since capillary blood is not identical to venous blood, there is a need to validate that both types of blood yield quality results in different applications. The aim of this study was to evaluate the agreement between capillary and venous blood for trace elements assessment and several hematological parameters. As, Cd, Ni, and Pb were determined in capillary and venous samples by Inductively Coupled Plasma Mass Spectrometry (ICP-MS) from 49 students (ages 18-36 years). Descriptive statistics and quality control parameters were calculated to review the data. The White Blood Cells (WBC) and Red Blood Cells (RBC) counts, the Hematocrit (Ht) %, and the Hemoglobin (Hb) levels were also analyzed. The precision of the method was between 6.1-15.2% for As, Cd, and Pb, while the accuracy was between 97.8-126.2% for the same elements. Ni concentrations remained below the Limits of Detection in 38% of the venous blood samples, affecting the relationship determination between the two types of blood. The results demonstrated high significant relationships for the elements of As (r2= 0.866), Cd (r2= 0.667), and Pb (r2= 0.966) between venous and capillary blood. The hematological parameters were highly similar. RBC and WBC counts in capillary blood were 4.67 1012/L and 6.70 109/L, and 4.76 1012/ L and 6.55 109/L for venous blood, respectively. The Ht and Hb levels for capillary blood were 40.3% and 13.76 g/dL, and 41.2% and 13.87 g/dL for venous blood. The agreement between venous and capillary blood was determined by constructing Bland-Altman plots. The mean differences were 0.073, -0.088, and -0.38 for As, Cd, and Pb. These results demonstrate the capacity of capillary blood to be used as an alternative biomarker and its potential to be used in cost-effective methods for exposure assessment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".