Laboratory management of perinatal patients with apparently “new” anti-D
Bibliographic record
Abstract
Despite the existence of long-standing, well-organized programs for Rh immune globulin (RhIG) prophylaxis, immune anti-D continues to be detected in the D– perinatal population. Between 2006 and 2008, 91 prenatal patients, found to have a previously unidentified anti-D, were followed up with a survey to their treating physician and with additional serologic testing where possible. The physician survey requested pregnancy and RhIG history information, including recent or distant potential alloimmunizing events, and the physicians were asked their opinion on the likely cause for the anti-D. Based on survey responses, updated RhIG information, and results of follow-up serology, anti-D was determined to be attributable to previously unreported RhIG in 44 of 91 (48.3%) cases and to active immunization (immune anti-D) in 36 of 91 cases (39.6%). A probable cause for alloimmunization was reported in 14 of 52 (26.9%) returned surveys. Anti-D alloimmunization continues to occur in our prenatal population despite a comprehensive approach to RhIG therapy. Observations from this prospective patient management strategy include the need for improved application of guidelines for RhIG administration and improved quality of information provided to laboratories assessing RhIG eligibility. A laboratory process for prospective follow-up when unexpected anti-D is detected in pregnancy is recommended.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".