A microcosting and cost consequence analysis from a randomized controlled trial comparing genome sequencing with exome sequencing for genetic diagnosis
Bibliographic record
Abstract
PURPOSE: Diagnosing rare diseases is costly. The objectives were to microcost exome (ES) and genome sequencing (GS) trios and estimate the incremental costs of GS per additional diagnosis from an institutional payer perspective. METHODS: Trios (proband plus biological parents) that are referred for sequencing were randomly assigned to ES or GS. Laboratory workflow and sequencing were microcosted. Total and category cost per trio were estimated probabilistically. Effectiveness was expressed as diagnostic yield (rates of diagnostic or partially diagnostic variants detected). Incremental costs and effectiveness were calculated. RESULTS: The mean total cost per trio was CAD 2888.79 (95% CI 2567.72, 3492.72) for ES (n = 329) and 4364.02 (95% CI 3984.94, 5013.67) for GS (n = 324). Reagents accounted for 34% and 61% of total costs for ES and GS, respectively. The incremental cost of GS was 1475.23. The diagnostic yield was 35.9% for ES and 32.7% for GS with a difference of 0.032 (95% CI: -0.041, 0.104, P value .397). CONCLUSION: GS demonstrated higher costs and a similar diagnostic yield to ES but was limited by technical capabilities at the time of the study. The study provides comprehensive costs for the economic evaluation comparing alternative diagnostic pathways and impetus for further evaluating variants uniquely detectable by GS.
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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 | 0.022 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.013 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".