Valuing the benefit of diagnostic testing for genetic causes of idiopathic developmental disability: willingness to pay from families of affected children
Bibliographic record
Abstract
Idiopathic developmental disability (DD) has been found to put significant psychological distress on families of children with DD. The cause of the disability, however, is unknown for up to one-half of the affected children. Chromosomal abnormalities identified by cytogenetic analysis are the most frequently recognized cause of DD, although they account for less than 10% of cases. Array genomic hybridization (AGH) is a new diagnostic tool that provides a much higher detection rate for chromosomal imbalance than conventional cytogenetic analysis. This increase in diagnostic capability comes at greater monetary costs, which provides an impetus for understanding how individuals value genetic testing for DD. This study estimated the willingness to pay (WTP) for diagnostic testing to find a genetic cause of DD from families of children with DD. A discrete choice experiment was used to obtain WTP values. When it was assumed that AGH resulted in twice as many diagnoses and a 1-week reduction in waiting time compared with conventional cytogenetic analysis, this study found that families were willing to pay up to CDN$1118 (95% confidence interval, $498-1788) for the expected benefit. These results support the conclusion that the introduction of AGH into the Canadian health care system may increase the perceived welfare of society, but future studies should examine the cost-benefit of AGH vs cytogenetic testing.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".