Rapid testing versus karyotyping in Down's syndrome screening: cost-effectiveness and detection of clinically significant chromosome abnormalities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it