Prevalence threshold and positive predictive value of noninvasive prenatal testing
Bibliographic record
Abstract
OBJECTIVE: Noninvasive prenatal testing (NIPT) has increased the number of conditions that can be screened. However, the prevalence of conditions assessed by NIPT has remained stable. The "prevalence threshold," a novel epidemiological concept, uses a test's sensitivity and specificity to determine the prevalence below which a test's positive predictive value declines most sharply relative to disease prevalence. In this article, we calculated the prevalence threshold for common conditions assessed through NIPT and compared the value with the actual prevalence of each condition to best ascertain the reliability of NIPT results. METHODS: Six databases and PubMed were searched from January 2010 to March 2023 for sensitivity and specificity parameters of common conditions tested through NIPT. Using an equation previously derived by the authors of the current paper, the prevalence threshold for each condition was calculated. The theoretical number of test iterations required to reach the prevalence threshold was also reported. RESULTS: None of the conditions tested through the NIPT had a prevalence rate that met or exceeded the calculated prevalence threshold. Trisomy 21 had the greatest concordance between the prevalence rate and the prevalence threshold. In contrast, Angelman, Cri-du-chat, and Prader-Willi syndromes had the most significant discordance. Apart from trisomy 21 and XXY, all remaining conditions required more than one test iteration to reach their respective prevalence threshold. CONCLUSION: We conclude that at the current prevalence levels, the positive predictive value of NIPT remains low, with the prevalence of disease levels significantly lower than the prevalence threshold for each condition tested.
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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 | 0.016 | 0.128 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| 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) | 0.002 | 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".