The role of molecular microsatellite identity testing to detect sampling errors in prenatal diagnosis
Bibliographic record
Abstract
OBJECTIVE: The objective was to determine the risk of sampling error in amniocentesis and chorionic villus sampling (CVS) in singleton and multiple pregnancies. Data from this and other published studies were used to discuss current practice guidelines for molecular identity testing. METHOD: Clinical and laboratory records of all patients undergoing molecular-based identity testing in our clinical laboratory from July 2002 until March 2008 were reviewed. DNA microsatellite testing was performed to determine zygosity in multiple pregnancies and maternal cell contamination (MCC) in both singleton and multiple pregnancies. RESULTS: MCC was detected in 6/148 (4%) CVS and 1/87 (1%) amniotic fluids from singleton pregnancies. In two of the CVS, only maternal cells were found. In 2/24 (8%) twin pregnancies, the same fetus was tested twice. In a total of 285 pregnancies (235 singleton, 24 twin, 26 with >or= 3 fetuses), without molecular identity testing, four women would have received erroneous results. CONCLUSION: Current guidelines recommend molecular identity testing for MCC in conjunction with molecular diagnostic testing, but not for cytogenetic testing. No published guidelines were found for zygosity testing in multiple pregnancies. We suggest that identity testing be considered for all prenatal testing of multiple pregnancies, especially if CVS is performed.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".