Referral patterns for microarray testing in prenatal diagnosis
Bibliographic record
Abstract
OBJECTIVE: To understand the prenatal referral patterns from the United States, Canada, and Israel for two whole-genome microarray platforms, each with a different resolution. METHOD: Physicians selected one of the two array designs to be performed on 1483 prenatal specimens for a 1-year period. We retrospectively examined detection rates, indications for study, and physician array selection. RESULTS: The lower resolution array (55 K) showed an ~32% decrease in the detection of results of unclear clinical significance while retaining the ability to detect all but one significant abnormality identified by the higher resolution array (135 K). A majority of samples were referred for abnormal ultrasound findings. Whereas the United States and Canada utilized the higher resolution array more often for this indication, Israel preferred the 55 K array. Referral patterns for parental anxiety were similar for the United States and Israel, with most cases being tested on the 55 K array. Few cases were referred for advanced maternal age or family history of a genetic condition from either Canada or Israel. CONCLUSION: Referral patterns varied between the countries and between indications for study. Understanding these differences will provide laboratories the critical information needed to develop array designs to meet the medical needs and patient desires for prenatal testing.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".