Use of Placental Growth Factor for Trisomy 21 Screening in Pregnancy: A Systematic Review
Bibliographic record
Abstract
Abstract Background Prenatal serum screening is an important modality to screen for aneuploidy in pregnancy. The addition of placental growth factor (PLGF) to screen for trisomy 21 remains controversial. Objective To determine whether the addition of PLGF to combined serum aneuploidy screening improves detection rates (DRs) for trisomy 21. Study Design We performed a systematic review of the literature until October 2019 to determine the benefits of adding PLGF to prenatal screening. We performed a goodness-of-fit test and retrieved the coefficient of determinations (R 2) as a function of false positive rates (FPRs), providing mean-weighted improvements in the DRs after accounting for PLGF levels. Results We identified 51 studies, of which 8 met inclusion criteria (834 aneuploidy cases and 105,904 euploid controls). DRs were proportional to FPR across all studies, ranging from 59.0 to 95.3% without PLGF and 61.0 to 96.3% with PLGF (FPR 1–5%). Goodness-of-fit regression analysis revealed a logarithmic distribution of DRs as a function of the FPR, with R 2 = 0.109 (no PLGF) and R 2 = 0.06 (PLGF). Two-sample Kolmogorov–Smirnov's test reveals a p-value of 0.44. Overall, addition of PLGF improves DRs of 3.3% for 1% FPR, 1.7% for 3% FPR, and 1.4% for 5% FPR, respectively. Conclusion Addition of PLGF to prenatal screening using serum analytes mildly improves trisomy 21 DRs as a function of FPRs.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".