[Application of multiple quantitative fluorescence polymerase chain reaction approach for rapid prenatal diagnosis of common chromosome aneuploidies].
Bibliographic record
Abstract
OBJECTIVE: To assess the value of multiple quantitative fluorescence polymerase chain reaction (QF-PCR) approach for rapid prenatal diagnosis of common chromosomal aneuploidies. METHODS: A total of 4760 amniotic samples from 4649 pregnant women were analyzed with QF-PCR for 21, 18, 13, X and Y aneuploidies, and the results were compared with those of karyotype analysis. RESULTS: The overall success rate for QF-PCR was 98.4%. All the 48 cases of 21, 18, 13, X and Y aneuploidies (including 2 case of 46, XY, rob(13:21), +21; 4 trisomy 21 in 4 twins) were detected by QF-PCR, with the overall sensibility and specificity both reaching 100%. One mosaicism of trisomy 21 and 4 mosaicisms of sex chromosome (1 misdiagnosed by karyotype analysis) were also detected by QF-PCR. Four mosaicisms of sex chromosome were verified as missed diagnosis. All the 64 cases failed by karyotype analysis were successfully analyzed by the QF-PCR approach. The total consistency rate for QF-PCR and karyotyping has reached 98.3%. CONCLUSION: QF-PCR approach can diagnose 21, 18, 13 as well as X and Y aneuploidies within 48 hours, in addition with a portion of mosaicisms. It is an efficient and reliable method for rapid prenatal diagnosis, and therefore provide an important supplement for karyotype analysis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".