Non-invasive prenatal aneuploidy testing: Critical diagnostic performance parameters predict sample z-score values
Bibliographic record
Abstract
Non-invasive prenatal aneuploidy testing (NIPT) by next-generation sequencing of circulating cell-free DNA in maternal plasma relies on chromosomal ratio (chrratio) measurements to detect aneuploid values that depart from euploid ratios. Diagnostic performances are known to depend on the fraction of fetal DNA (FF) present in maternal plasma, although how this translates into specific quantitative changes in specificity/positive predictive values and which other variables might also be important is not well understood. To explore this issue, theoretical relationships between FF and various measures of diagnostic performances were assessed for a range of parameter values. Empirical data from three NIPT assays were then used to validate theoretical calculations. For a given positivity threshold, dramatic changes in specificity and positive predictive values (PPV) as a function of both FF and the coefficient of variation (CV) of the chrratio measurement were observed. Theoretically predicted and observed chrratio z-scores agreed closely, confirming the determinant impact of small changes in both FF and chrratio CV. Evaluation of NIPT assay performances therefore requires knowledge of the FF distribution in the population in which the test is intended to be used and, in particular, of the precise value of the assay chrratio CV for each chromosome or genomic region of interest. Laboratories offering NIPT testing should carefully measure these parameters to ensure test reliability and clinical usefulness in interpreting individual patients' results.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".