Rapid testing versus karyotyping in Down's syndrome screening: cost-effectiveness and detection of clinically significant chromosome abnormalities
Bibliographic record
Abstract
In all, 80% of antenatal karyotypes are generated by Down's syndrome screening programmes (DSSP). After a positive screening, women are offered prenatal foetus karyotyping, the gold standard. Reliable molecular methods for rapid aneuploidy diagnosis (RAD: fluorescence in situ hybridization (FISH) and quantitative fluorescence PCR (QF-PCR)) can detect common aneuploidies, and are faster and less expensive than karyotyping.In the UK, RAD is recommended as a standalone approach in DSSP, whereas the US guidelines recommend that RAD be followed up by karyotyping. A cost-effectiveness (CE) analysis of RAD in various DSSP is lacking. There is a debate over the significance of chromosome abnormalities (CA) detected with karyotyping but not using RAD. Our objectives were to compare the CE of RAD versus karyotyping, to evaluate the clinically significant missed CA and to determine the impact of detecting the missed CA. We performed computer simulations to compare six screening options followed by FISH, PCR or karyotyping using a population of 110948 pregnancies. Among the safer screening strategies, the most cost-effective strategy was contingent screening with QF-PCR (CE ratio of $24084 per Down's syndrome (DS) detected). Using karyotyping, the CE ratio increased to $27898. QF-PCR missed only six clinically significant CA of which only one was expected to confer a high risk of an abnormal outcome. The incremental CE ratio (ICER) to find the CA missed by RAD was $66608 per CA. These costs are much higher than those involved for detecting DS cases. As the DSSP are mainly designed for DS detection, it may be relevant to question the additional costs of karyotyping.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".