Survey of laboratory tests used in the diagnosis and evaluation of haemophilia A
Bibliographic record
Abstract
Although the incidence of haemophilia A is reportedly uniform across ethnic groups, the prevalence varies in different countries. This suggests variability in the effectiveness of diagnostic strategies which is of particular importance in the recognition of milder forms of the disease. To assess the different laboratory tests that are used in the diagnosis and subsequent management of haemophilia A we carried out a web-based survey of established haemophilia centres. This was sent to 13 haemophilia physicians from haemophilia-treatment centres in Germany, Italy, Spain, South Africa, Taiwan, Norway, Canada, UK and the USA. The survey asked for details of clotting tests, the use of genetic analysis and the use of global haemostatic assays in haemophilia A cases. The results show considerable variation in the laboratory methods used for the screening, diagnosis and monitoring of haemophilia A. There is variability in the techniques used even for long-standing, standardised assays such as the one-stage factor assay. There is marked regional variability in the use of molecular diagnosis. Assessment of haemophilia A requires accurate and sensitive assays. Some laboratories continue to rely on a single-factor assay in the diagnosis of non-severe disease, although cases with assay discrepancy may be missed by this strategy. Global assays are becoming important in the evaluation and management of patients. However, standardisation and the correlation with clinical outcomes require further study. Genetic diagnosis in patients with haemophilia remains underutilised in USA, possibly because of a lack of funding.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".