Costs and benefits of non‐invasive fetal <scp>RhD</scp> determination
Bibliographic record
Abstract
OBJECTIVE: Non-invasive fetal Rhesus (Rh) D genotyping, using cell-free fetal DNA (cffDNA) in the maternal blood, allows targeted antenatal anti-RhD prophylaxis in unsensitized RhD-negative pregnant women. The purpose of this study was to determine the cost and benefit of this approach as compared to routine antenatal anti-RhD prophylaxis for all unsensitized RhD-negative pregnant women, as is the current policy in the province of Alberta, Canada. METHODS: This study was a decision analysis based on a theoretical population representing the total number of pregnancies in Alberta over a 1-year period (n = 69 286). A decision tree was created that outlined targeted prophylaxis for unsensitized RhD-negative pregnant women screened for cffDNA (targeted group) vs routine prophylaxis for all unsensitized RhD-negative pregnant women (routine group). Probabilities at each decision point and costs associated with each resource were calculated from local clinical and administrative data. Outcomes measured were cost, number of women sensitized and doses of Rh immunoglobulin (RhIG) administered. RESULTS: The estimated cost per pregnancy for the routine group was 71.43 compared with 67.20 Canadian dollars in the targeted group. The sensitization rates per RhD-negative pregnancy were equal, at 0.0012, for the current and targeted programs. Implementing targeted antenatal anti-RhD prophylaxis would save 4072 doses (20.1%) of RhIG over a 1-year period in Alberta when compared to the current program. CONCLUSIONS: These data support the feasibility of a targeted antenatal anti-RhD prophylaxis program, at a lower cost than that of the existing routine prophylaxis program, with no increased risk of sensitization.
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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 | 0.000 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".