The Genetics of Hemophilia: Analysis of Patients at the Hospital for Sick Children, Toronto, Canada.
Bibliographic record
Abstract
Abstract There is great interest in identifying genetic mutations responsible for hemophilia and in determining if/how mutations correlate with disease phenotype. A hemophilia genetic database was created at SickKids in 2004. At the time <40% of hemophiliacs followed by the clinic had been genotyped. Currently mutations have been identified on 194/236 patients (82%) followed in the clinic. From this we are performing genotype/phenotype correlations. Preliminary analysis has revealed the following novel findings: Most mothers of hemophiliacs are carriers; even when there is a no family history of hemophilia. Of the 199 mothers of the 236 children only 6 have been shown not to be carriers; 205 have been shown to be carriers; 15 have not been tested and 10 are unavailable for testing. We believe that most de novo FIX and FVIII mutations occur in females. Mutations responsible for hemophilia B show poor concordance with disease severity i.e. for any mutation the disease severity is not always the same. One example found in 10 hemophiliacs (5 families) who despite having the same missense mutation in exon H have shown FIX levels anywhere between 0 and 11%. Given that disease severity is assigned according to factor levels these patients have been labeled as either severe (n = 3), moderate (n = 5) or mild (n = 2). Clinically these patients all appear to behave as moderate. This points to the fallibility of using FIX levels (which varies according to patient age and health state) in labeling patients. Other aspects of the hemophilia genetic data base are being analyzed. We believe that a detailed study of the genetics of hemophilia will point to novel findings that will eventually translate into patient care.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".