Added value of chromosomal microarray analysis over karyotyping in early pregnancy loss: systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: To estimate the increased test success rate and incremental yield of chromosomal microarray analysis (CMA) over conventional karyotyping in detection of pathogenic copy number variants (CNVs) and variants of unknown significance (VOUS) in early pregnancy loss. METHOD: This was a systematic review conducted in accordance with PRISMA criteria. All articles identified in PubMed, Ovid MEDLINE and Web of Science, between January 2000 and April 2017, that described CNVs in early pregnancy losses (up to 20 weeks) were included. Risk differences were pooled to estimate the incremental yield of CMA over karyotyping overall, and after stratification. In addition, test success rate, defined as the proportion of informative results, was compared in series in which CMA and karyotyping were performed concurrently. RESULTS: Twenty-three studies, reporting on 5507 pregnancy losses up to 20 weeks with full data available, met the inclusion criteria for analysis. In the series in which CMA and karyotyping were performed concurrently, CMA showed a significant improvement in success rate, providing informative results in 95% (95% CI, 94-96%) of cases compared with karyotyping in which informative results were provided in 68% (95% CI, 66-70%) of cases. Combined data from reviewed studies revealed that incremental yields of CMA over karyotyping were 2% (95% CI, 1-2%) for pathogenic CNVs and 4% (95% CI, 3-6%) for VOUS. The most common pathogenic CNVs reported were 22q11.21 and 1p36.33 deletion. CONCLUSION: In comparison with conventional karyotyping, CMA provides a significant increase in test success rate and incremental diagnostic yield in early pregnancy loss. Copyright © 2017 ISUOG. Published by John Wiley & Sons Ltd.
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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 | 0.015 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.025 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".