The complexity of diagnosing rare disease: An organizing framework for outcomes research and health economics based on real-world evidence
Bibliographic record
Abstract
PURPOSE: To facilitate robust economic analyses of clinical exome and genome sequencing, this study was taken up with the objective of establishing a framework for organizing diagnostic testing trajectories for patients with rare disease. METHODS: We collected diagnostic investigations-related data before exome sequencing from the medical records of 228 cases. Medical geneticist experts participated in a consensus building process to develop the SOLVE Framework for organizing the complex range of observed tests. Experts categorized tests as indicator or nonindicator tests on the basis of their specificity for diagnosing rare diseases. Face validity was assessed using case vignettes. RESULTS: Most cases had symptom onset at birth (42.5%) or during childhood (43.4%) and had intellectual disability (73.3%). On average, the time spent seeking a diagnosis before sequencing was 1989 days (SD = 2137) and included 16 tests (SD = 14). Agreement across experts on test categories ranged from 83% to 96%. The SOLVE Framework comprised observed tests, including 186 indicator and 39 nonindicator tests across cytogenetic/molecular, biochemical, imaging, electrical, and pathology test categories. CONCLUSION: Real-world diagnostic testing data can be ascertained and organized to reflect the complexity of the journey of the patients with rare diseases. SOLVE Framework will improve the accuracy and certainty associated with value-based assessments of genomic sequencing.
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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 | 0.089 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.006 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".