Digital Microfluidic Hemagglutination Assays for Blood Typing, Donor Compatibility Testing, and Hematocrit Analysis
Bibliographic record
Abstract
BACKGROUND: Blood typing, donor compatibility testing, and hematocrit analysis are common tests that are important in many clinical applications, including those found in high-stakes settings such as the trauma center. These tests are typically performed in centralized laboratories with sample batching; the minutes that are lost in this mode can lead to adverse outcomes, especially for critical-care patients. As a step toward providing rapid results at the bedside, we developed a point-of-care hemagglutination system relying on digital microfluidics (DMF) and a unique, automated readout tool, droplet agglutination assessment using digital microfluidics (DAAD). METHODS: ABO and Rhesus blood grouping, donor crossmatching, and hematocrit assays were developed on a portable DMF platform that allowed for automated sample processing. The result of each assay could be determined by eye or automatically with the DAAD imaging tool. RESULTS: DMF-DAAD was applied to 109 samples collected from different sources (including commercial samples, pinpricks from volunteers, and a hospital blood bank), with perfect fidelity to gold-standard results. Some of these tests were carried out by a nonexpert in a hospital trauma center. Proof-of-concept results were also collected from smaller sample sets for donor compatibility testing and hematocrit analysis. CONCLUSION: DMF-DAAD shows promise for delivering rapid, reliable results in a format well suited for a trauma center and other settings where every minute counts.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".