Using Capillary Whole Blood to Quantitatively Measure Ferritin: A Validation Trial of a Point-of-Care System
Bibliographic record
Abstract
Iron deficiency is a public health problem with devastating health, developmental and behavioral effects which often exacerbated due to affordability and access to screening and diagnosis. Using IronScan™ a portable, point-of-care diagnostic system capable of quantitatively measuring ferritin in blood, we validated IronScan™ ferritin measurements using whole blood and serum with a lab-based, regulator-approved analytical device for measuring ferritin in venous serum. Capillary (finger stick) and venous whole blood samples were obtained from 44 male and female volunteers. Venous serum (vSer) ferritin concentrations were measured on Immulite 2000 Xpi (gold standard). Capillary whole blood (cWB), venous whole blood (vWB), and vSer ferritin levels were measured by IronScan™. cWB ferritin concentrations from IronScan™ were significantly correlated (R2 = 0.86) with vSer measured with the FDA-approved Immulite system. The results from the multiple regression analysis indicate that 10% of the variability was due to the method of blood collection (venous vs. capillary) and 6% was due to the form of blood analysis (whole blood vs. serum). The sensitivity of diagnosing iron deficiency using the WHO cutoff of <30 ng/mL is 90%, with a specificity of 96%. In conclusion, IronScan™ is a rapid viable option for measuring ferritin as a point-of-care system.
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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 | 0.028 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".