Conventional tube agglutination with polyethylene glycol versus Red Cell Affinity Column Technology (ReACT): a comparison of antibody detection methods.
Bibliographic record
Abstract
A procedure for antibody detection and identification that utilizes affinity microcolumns to isolate IgG antibodies in a gel matrix containing Protein G and Protein A was commercially available in recent years. We evaluated this method (ReACT, Red Cell Affinity Column Technology, Immucor Co., Norcross, GA) as an alternative to standard tube agglutination testing, in an effort to minimize subjectivity and increase consistency of antibody identification in our hospital blood banks. Although the ReACT kit was withdrawn from the market soon after completion of our study, the advantages and limitations of the procedure warrant consideration should a similar product be reintroduced. The performance of the ReACT method was compared to conventional antibody detection by a standard tube agglutination technique that uses polyethylene glycol (PEG) potentiator (Dominion Biologicals Ltd., Dartmouth, Nova Scotia, Canada). Of 685 serum or plasma samples that were screened for antibodies, 96 samples were found by the PEG procedure to contain clinically significant (n = 70) and insignificant antibodies (n = 26). In contrast, 48 of the samples were found by the ReACT procedure to contain clinically significant (n = 39) and clinically insignificant antibodies (n = 9). For the ReACT method, the sensitivity was 48.8% (95% CI = 37.8%, 58.0%) and the specificity was 99.6% (95% CI = 97.5%, 99.9%), compared to the PEG procedure. While the ReACT microcolumn system was designed to limit detection of clinically insignificant antibodies, this study documents a loss of sensitivity for detection of clinically significant antibodies.
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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 | 0.001 |
| 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".