Comparison of prenatal anti‐D titration testing by gel and tube methods: A review of the literature
Bibliographic record
Abstract
BACKGROUND: Antenatal titration testing is traditionally performed using a manual tube test. Tube testing has limitations; it is a manual, time-consuming method with wide interobserver variability. Gel-based testing is an attractive alternative because it is more precise and can be automated. This study's objective was to summarize the published literature that assessed the relationship between titrations performed by tube and gel for anti-D alloimmunized pregnancies. STUDY DESIGN AND METHODS: A comprehensive literature search was performed. Articles were selected if research was original and compared at least five pairs of anti-D titration tests performed by gel and tube. Differences in the number of dilutions between gel and tube methods were compared overall by study and cell type using linear models. RESULTS: A total of 512 articles were identified; eight were included, and titer data from 384 tube and gel pairs were abstracted. The median anti-D titer in tube was 8 (range 0-2048) and by gel was 64 (range 0-4096). Anti-D gel titration results were 2.1 (95% CI; 1-3.3) additional dilutions greater than in tube. Most studies utilized double-dose reagent cells for testing. At a tube titer of 16, the sensitivity and specificity of gel titrations is maximal (91% and 94% respectively) at a gel titer of 64. CONCLUSION: Overall, titrations performed by gel were two dilutions higher than the corresponding tube titer. For titrations, double-dose reagent cells should be considered to standardize practice. A rigorous prospective study is needed to compare tube titrations with gel titrations using a standardized process.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".