Expansion of non-invasive prenatal screening to the screening of 10 types of chromosomal anomalies: a cost-effectiveness analysis
Bibliographic record
Abstract
OBJECTIVES: To determine the cost-effectiveness of the addition of chromosomal anomalies detectable by non-invasive prenatal screening (NIPS), in a prenatal screening programme targeting common aneuploidies. DESIGN, SETTING AND PARTICIPANTS: A simulation study was conducted to study the addition of chromosomal anomalies detectable by NIPS (sex chromosome aneuploidies, 22q11.2 deletion syndrome, large deletion/duplication >7 Mb and rare autosomal trisomies) to five basic strategies currently aiming the common trisomies: three strategies currently offered by the public healthcare systems in Canada, whose first-tier test is performed with biochemical markers, and two programmes whose first-tier test consists of NIPS-based methods. OUTCOME MEASURES: The total number of cases of chromosomal anomalies detected and the costs related to the consumption of medical services. RESULTS: The most effective and the most cost-effective option in almost all prenatal screening strategies is the option that includes all targeted additional conditions. In the strategies where NIPS is used as first-tier testing, the cost per additional case detected by adding all possible additional anomalies to a programme that currently targets only common trisomies is $C25 710 (95% CI $C25 489 to $C25 934) for massively parallel shotgun sequencing and $C57 711 (95% CI $C57 141 to $C58 292) for targeted massively parallel sequencing, respectively. The acceptability curves show that at a willingness-to-pay of $C50 000 per one additional case detected, the expansion of NIPS-based methods for the detection of all possible additional conditions has a 90% probability of being cost-effective. CONCLUSION: From an economic perspective, in strategies that use NIPS as a first-tier screening test, expanding the programmes to detect any considered chromosomal anomalies other than the three common trisomies would be cost-effective. However, the potential expansion of prenatal screening programmes also requires consideration of societal issues, including ethical ones.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".