Microarray as a first genetic test in global developmental delay: a cost-effectiveness analysis
Bibliographic record
Abstract
AIM: Microarray technology has a significantly higher clinical yield than karyotyping in individuals with global developmental delay (GDD). Despite this, it has not yet been routinely implemented as a screening test owing to the perception that this approach is more expensive. We aimed to evaluate the effect that replacing karyotype with array-based comparative genomic hybridization (aCGH) would have on the total cost of the workup for GDD. METHOD: We evaluated the cost-effectiveness of aCGH compared with karyotyping by retrospectively analysing the cost of workup in a cohort of 114 children (69 males; 45 females) representing a consecutive series of children diagnosed with GDD. RESULTS: The average increase in cost if aCGH had been performed instead of karyotyping as a first test was $442 per individual when performed by a private company (98% confidence interval $238-604). In contrast, $106 (98% confidence interval -$17 to $195) would have been saved if aCGH was performed locally in a laboratory already possessing the required technology. The incremental cost per additional diagnosis was estimated to be $12,874 if aCGH was performed in a private laboratory, but <$1379 if performed locally. (Costs reported in Canadian dollars, using 2010 prices.) INTERPRETATION: aCGH would be cost-effective as a first genetic test in the clinical evaluation of individuals with GDD.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".