Z-score-based posttest risk as an alternative risk metric to positive predictive value following positive noninvasive prenatal screening
Bibliographic record
Abstract
Noninvasive prenatal screening is a long-established and widely used methodology to screen pregnancies for the most common prenatal chromosomal aneuploidies. Since 2017, positive result reports have typically included a positive predictive value to assist informed clinical decision-making. Positive predictive value is calculated based on an assay's sensitivity and specificity for a particular condition, and for the purpose of noninvasive prenatal screening, the aneuploidy's prevalence by maternal age, sometimes further adjusted by gestational age, are included in the calculation. Considering the ubiquitous use of positive predictive value by major noninvasive prenatal screeningproviders in the US, it is important to consider its limitations and consequent clinical implications. Here we discuss how the calculation of positive predictive value for screen positive results precludes the ability of positive predictive value to act as a risk metric that is accurate for and specific to an individual pregnancy, and suggest posttest risk based on the amount of target chromosome excess (Z-score-based posttest risk) as an alternative metric for consideration.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".