Laboratory testing for bleeding disorders: strategic uses of high and low‐yield tests
Bibliographic record
Abstract
Laboratory testing is essential for diagnosing bleeding disorders. The tests and panels that laboratories currently use for bleeding disorder evaluation are not standardized, although most offer coagulation screening tests in bleeding disorder panels. Some tests for bleeding disorders, including von Willebrand factor multimer assays and tests for rarer disorders, are not widely available. Accordingly, clinicians and laboratories need tailored strategies for evaluating common and rare bleeding disorders. Coagulation screening tests have high specificity, however, false positives and false negatives do occur among subjects evaluated for bleeding disorders and more specific tests (e.g., factor assays) are required to further assess abnormalities. Tests for defects in primary hemostasis have similar high specificity but much greater sensitivity for common bleeding disorders than coagulation screening tests. Nonetheless, extensive testing fails to establish a diagnosis in a significant number of individuals considered to have significant bleeding problems. Rare bleeding disorder investigations are important to diagnose some conditions, particularly those with delayed-onset bleeding, such as factor XIII deficiency, α2 antiplasmin deficiency, plasminogen activator inhibitor-1 deficiency, and Quebec platelet disorder. These issues need careful consideration when assessing patients for congenital and acquired bleeding problems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".