The detection of chromosome anomalies by QF‐PCR and residual risks as compared to G‐banded analysis
Bibliographic record
Abstract
OBJECTIVE: To determine the detection rate of clinically significant chromosome abnormalities using quantitative fluorescent polymerase chain reaction (QF-PCR) of fetal DNA in comparison with G-banded analysis of cultured amniotic fluid cells and determine the residual risk if QF-PCR were performed alone for low-risk cases. METHODS: Amniotic fluid samples were prospectively categorized based on the likelihood of the fetus having a chromosome anomaly. QF-PCR results were compared with the G-banded findings. The distribution of patients and the rates of clinically significant anomalies in each risk category were determined. RESULTS: A total of 4176 amniotic fluid samples were studied. Among these, 331 cases with abnormalities were detected by both methods and an additional 19 abnormal cases were detected by G-banding only. Five of those undetected by QF-PCR were considered clinically significant, four of which were referred due to an elevated a priori risk (>4%). If QF-PCR is performed in all cases and G-banding performed only in higher risk cases, the residual risk for a clinically significant chromosome abnormality will be as low as 0.083%. CONCLUSIONS: This study suggests that QF-PCR alone is appropriate for patients with uncomplicated pregnancies, who are referred solely for an increased risk of a common trisomy.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".