Genomic testing for bleeding disorders (GT4BD): protocol for a randomised controlled trial evaluating the introduction of whole genome sequencing early in the diagnostic pathway for patients with inherited bleeding disorders as compared with standard of care
Bibliographic record
Abstract
INTRODUCTION: The current diagnostic pathway for patients with a suspected inherited bleeding disorder is long, costly, resource intensive, emotionally draining for patients and often futile, as half of patients will remain without a diagnosis and be labelled 'bleeding disorder of unknown cause'. Advances in understanding the genetic basis of the inherited bleeding disorders, coupled with both increasing infrastructure for genetic/genomic testing and decreasing costs, have increased the feasibility of introducing genomic testing into the clinical diagnostic pathway as a potential solution to improve the care of these patients. Yet, there remain evidence gaps on the optimal integration of genomic analysis into the diagnostic pathway. METHODS AND ANALYSIS: Using a multicentre randomised-controlled trial design, we will evaluate an early genomic testing strategy for the diagnosis of newly referred patients with a suspected inherited bleeding disorder. Eligible participants will be randomised to early genomic testing diagnostic pathway (intervention) or standard diagnostic pathway (control) and will be followed for a 12-month period. Patients in the control group who remain undiagnosed at study end will be offered identical early genomic testing to ensure equitable access to the intervention. The study will follow a parallel fixed design with waitlist control group and a 1:1 allocation ratio. The study will be conducted at three tertiary care centres in Ontario, Canada, with a target sample size of 212 participants. Clinical utility will be evaluated via the primary outcome of diagnostic yield, as well as the secondary outcome of time to diagnosis. Additional secondary outcomes will allow for assessment of patient impact via health-related quality of life and patient burden measures, as well as evaluation of economic impact through a cost-effectiveness analysis and budget impact analysis. ETHICS AND DISSEMINATION: This investigator-initiated study was approved by the Queen's University Health Sciences and Affiliated Teaching Hospitals Research Ethics Board through Clinical Trials Ontario (CTO-4909). Participant informed consent/assent is required. Findings will be disseminated through academic publications. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov, NCT06736158.
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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 | 0.043 | 0.046 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.007 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.072 | 0.014 |
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".