An algorithm for the prenatal detection of chromosome anomalies by QF‐PCR and G‐banded analysis
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to examine in theory the clinical utility of a prenatal algorithm that uses rapid aneuploidy detection in all cases and G-banded analysis for selected cases (RAD/G algorithm). METHODS: Over a 4-year period, amniotic fluid samples were prospectively assigned into RAD (limited analysis) or RAD/G (intensive analysis) categories based upon the likelihood of the fetus having a chromosome anomaly. The samples were cultured and analyzed by standard cytogenetic methods. The rates of clinically significant chromosomal anomalies potentially undetectable by the RAD/G algorithm were calculated. RESULTS: The karyotype was normal in 3861/4054 (95.24%) cases and abnormal in 193 (4.76%). From these data, the detection rate of the RAD/G algorithm was 87.6% if all abnormalities detected by G-banding were taken into consideration and 97.6% if abnormalities having reduced predictive value were excluded (balanced rearrangements and most mosaic cases). CONCLUSIONS: Compared to G-banding alone, the RAD/G algorithm has a reduction in sensitivity due to undetectable abnormalities and mosaicism in the RAD group. However, it provides a rapid and inexpensive alternative to traditional G-banded analysis, and might be more appropriate for patients with uncomplicated, low risk pregnancies.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".