Use of Placental Growth Factor (PLGF) for Aneuploidy Screening in Pregnancy: A Systematic Review [38K]
Bibliographic record
Abstract
INTRODUCTION: Placental growth factor (PLGF) is a protein of trophoblastic origin. Whether addition of PLGF to combined serum aneuploidy screening tests improves detection rates for trisomy 21 is controversial. METHODS: We performed a systematic review of the literature until August 2019. We performed a goodness of fit test and retrieved the coefficient of determinations (R2) before and after the introduction of PGLF, as a function of false positive rates. Receiver-operating characteristic curves are provided in order to illustrate findings. RESULTS: We identified a total of 51 studies, of which 8 met inclusion criteria. Three prospective and five case-control studies included a total of 834 aneuploidy cases and 105,904 euploid control pregnancies. Detection rates (DR) were proportional to false-positive rates (FPR) across all studies, and ranged from 59.0% to 95.3% without the use of PLGF and 61.0% to 96.3% with PLGF (FPR 1% to 5%). Goodness of fit regression analysis revealed a logarithmic distribution of DR with R2=0.109 in the no PLGF group and R2=0.06 in the PLGF group. These indicate a large variation between DR for a given FPR across studies. Our analysis demonstrates average improvements in DR of 3.3% for a 1% FPR, 1.7% for a 3% FPR, and 1.4% for a 5% FPR. CONCLUSION: Addition of PLGF to prenatal screening using serum analytes mildly improves trisomy 21 detection rates between 1.4 and 3.3% for FPR’s between 5% and 1%, respectively. Future studies should address the cost-benefit analysis of introducing PLGF for prenatal screening.
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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 | 0.009 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".