Trio genome sequencing for developmental delay and pediatric heart conditions: A comparative microcost analysis
Bibliographic record
Abstract
PURPOSE: Genome sequencing (GS) can aid clinical management of multiple pediatric conditions. Insurers require accurate cost information to inform funding and implementation decisions. The objective was to compare the laboratory workflows and microcosts of trio GS testing in children with developmental delay (DD) and in children with cardiac conditions. METHODS: Cost items related to each step in trio GS (child and 2 parents) for both populations were identified and measured. Program costs over 5 years were estimated. Probabilistic and deterministic analyses were conducted. RESULTS: The mean cost per trio GS was CAD$6634.11 (95% CI = 6352.29-6913.40) for DD and CAD$8053.10 (95% CI = 7699.30-8558.10) for cardiac conditions. The 5-year program cost was CAD$28.11 million (95% CI = 26.91-29.29) for DD and CAD$5.63 million (95% CI = 5.38-5.98) for cardiac conditions. Supplies constituted the largest cost component for both populations. The higher cost per sample for the population with cardiac conditions was due to the inclusion of pharmacogenomics, higher bioinformatics labor costs, and a more labor intensive case review. CONCLUSION: This analysis indicated important variation in trio GS workflow and costs between pediatric populations in a single institution. Enhanced understanding of the clinical utility and costs of GS can inform harmonization and implementation decision-making.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".