A gel technology system to determine postpartum RhIG dosage
Bibliographic record
Abstract
Failures of Rh immune globulin (RhIG) prophylaxis occur when the dose is too small. We report a test using a gel technology (GT) method to replace the Kleihauer-Betke (K-B) test to assess fetomaternal hemorrhage (FMH) and assist in determining the minimum necessary dose of RhIG. Cord blood (O, D+) was mixed with adult blood (O D-) to mimic an FMH of 10 mL, 20 mL, 28 mL, and 40 mL. Test samples were incubated with anti-D at known concentrations and centrifuged. The supernatant was titrated against D+ and D- red cells using GT and an interpretation of the required RhIG dose was made. Results were compared with the K-B test. Results were easily discernible and interpretations leading to determination of recommended RhIG dosage were reproducible. Correlation to standard K-B testing was confirmed. Elapsed time for result availability by GT testing was 60 minutes, with a direct technical time requirement of 30 minutes. The GT system is easier, objective, and quantitative, and compares well to the standard K-B test. A single procedure will allow assessment of the extent of FMH in the great majority of cases. This technique works well in determining the appropriate dose of anti-D required to treat D- patients with D+ newborns. There are potential cost savings in decreased use of RhIG, less direct technical time required, and more rapid availability of results.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".