Genetic sequence analysis of inherited bleeding diseases
Bibliographic record
Abstract
The genes encoding the coagulation factor proteins were among the first human genes to be characterized over 25 years ago. Since then, significant progress has been made in the translational application of this information for the 2 commonest severe inherited bleeding disorders, hemophilia A and B. For these X-linked disorders, genetic characterization of the disease-causing mutations is now incorporated into the standard of care and genetic information is used for risk stratification of treatment complications. With electronic databases detailing >2100 unique mutations for hemophilia A and >1100 mutations for hemophilia B, these diseases are among the most extensively characterized inherited diseases in humans. Experience with the genetics of the rare bleeding disorders is, as expected, less well advanced. However, here again, electronic mutation databases have been developed and provide excellent guidance for the application of genetic analysis as a confirmatory approach to diagnosis. Most recently, progress has also been made in identifying the mutant loci in a variety of inherited platelet disorders, and these findings are beginning to be applied to the genetic diagnosis of these conditions. Investigation of patients with bleeding phenotypes without a diagnosis, using genome-wide strategies, may identify novel genes not previously recognized as playing a role in hemostasis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".