Bibliographic record
Abstract
The genes encoding the coagulation factors were characterized over two decades ago. Since then, significant progress has been made in the genetic diagnosis of the two commonest severe inherited bleeding disorders, haemophilia A and B. Experience with the genetic of inherited rare bleeding disorders and platelet disorders is less well advanced. Rare bleeding disorders are usually inherited as autosomal recessive disorders, while it is now clear that a number of the more common platelet function disorders are inherited as autosomal dominant traits. In both cases, DNA sequencing has been useful since most of these disorders are due to mutations located at the coding regions or splice sites of genes encoding the abnormal protein. However, in 5-10% of patients affected with severe clotting factor deficiencies, no genetic defect can be identified and until recently, the genetic characterization of inherited platelet disorders had been confined to the more prevalent conditions such as Glanzmann disease and Bernard-Soulier syndrome. In patients with no gene mutations identified, so far, the role of next-generation sequencing as well as of other new genomic technologies will very likely have increasing importance. However, such methods require extensive bioinformatics analysis that, in turn will require critical revision of our current diagnostic infrastructure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".