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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".