Comparison of automated solid phase versus manual saline indirect antiglobulin test methodology for non‐<scp>ABO</scp> antibody titration: Implications for perinatal antibody monitoring
Bibliographic record
Abstract
BACKGROUND: Accurate antibody titration is crucial in prenatal evaluations to identify patients who need clinical monitoring for hemolytic disease of the fetus and newborn (HDFN) causing fetal anemia. This study compares the established gold standard method of manual tube saline indirect antiglobulin testing (SIAT) with the newer automated solid phase (ASP) method of antibody titration and aims to establish the critical titer threshold for ASP that corresponds to the previously established SIAT critical threshold of ≥16 used in our laboratory. STUDY DESIGN AND METHODS: One hundred fifty-seven prenatal and donor plasma samples with known antibodies were tested using both SIAT and ASP methodologies and results were compared. RESULTS: The study found that ASP titers were, on average, 1.33 dilutions higher than SIAT titers. The critical titer cutoff for ASP was determined to be ≥32, which is one tube higher than the SIAT cutoff of ≥16. DISCUSSION: The ASP method for antibody titration offers greater reproducibility and efficiency compared with manual SIAT titration. This study suggests that a titer cutoff of ≥32 is appropriate for most clinically significant antibodies using ASP. However, further research is needed to determine the comparability of ASP with SIAT in samples with multiple antibodies, anti-M antibodies, and other less common antibodies. Validation of the ASP titer cutoff against HDFN clinical outcomes is required before implementing this test for routine use in perinatal antibody titration.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".