The application of genetics to inherited bleeding disorders
Bibliographic record
Abstract
SUMMARY: Most bleeding disorders encountered in clinical practice will be diagnosed, at least initially, by phenotypic assays. However, since the characterization of the genes that encode coagulation factors in the 1980s, significant progress has been made in translating this knowledge for diagnostic and therapeutic purposes. For the haemophilias, in particular, molecular genetic testing to determine carrier status, prenatal diagnosis and prediction of the likelihood of inhibitor development has now become an established component of comprehensive clinical management. For von Willebrand's disease (VWD), significant recent advances have allowed for the establishment of genotype-phenotype correlations that have improved our understanding of the disease. The availability of high density single nucleotide polymorphism (SNP) maps will allow investigators to probe the genetic basis of the general symptoms of bleeding and bruising using a comprehensive genome-wide approach. This article will review the state-of-the-art for molecular diagnostics for both haemophilia and VWD and will end with a discussion of plans for an international genome-wide association study (GWAS) designed to improve our understanding of blood coagulation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".