Approaches to investigating common bleeding disorders: An evaluation of North American coagulation laboratory practices
Bibliographic record
Abstract
Bleeding disorders commonly result from deficiencies or defects in von Willebrand factor (VWF), platelets, coagulation factors, or fibrinolytic proteins. The primary goal of our study was to assess current North American coagulation laboratory practices for diagnosing bleeding disorders, using an on-line patterns-of-practice survey of diagnostic laboratory members of the North American Specialized Coagulation Laboratory Association. The survey examined laboratory approaches to evaluating bleeding disorders, with specific questions about the tests and test panels offered and compliance to recent guideline recommendations on diagnosing von Willebrand disease (VWD) and platelet function disorders. All laboratories responding to the survey performed a prothrombin time/international normalized ratio, an activated partial thromboplastin time, and coagulation factor assays, and many tested for VWD and platelet disorders. However, few laboratories had test panels that evaluated the more common bleeding disorders and few performed some assays, including VWF multimer assessments and assays for fibrinolytic disorders. Additionally, the cutoffs used by laboratories to diagnose type 1 VWD varied considerably, with only a minority following the National Heart Lung Blood Institute recommendations. In contrast, laboratories that tested for platelet function disorders mostly complied with aggregation testing recommendations, as published in the recent North American guidelines. Our results indicate that there are some gaps in the strategies used by laboratories to diagnose bleeding disorders that might be addressed by development of further guidelines and test algorithms that emphasize evaluations for common bleeding disorders. Laboratories may also benefit from guidelines on test interpretation, and external evaluation of their bleeding disorder testing strategies.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".