Diagnosis of bleeding disorder of unknown cause: how many tests are enough to diagnose BDUC?
Bibliographic record
Abstract
The challenges associated with achieving a clear diagnosis in patients with a suspected bleeding disorder are evident in those who end up categorized as bleeding disorder of unknown cause (BDUC), which can contribute to uncertainty in management and suboptimal care. BDUC is a diagnosis of exclusion, with nondiagnostic first-line hemostatic laboratory testing not meeting the criteria of an inherited mild bleeding disorder, despite the patient having a positive bleeding phenotype and/or positive family history. An abnormal bleeding phenotype, an important diagnostic criterion for BDUC, should be assessed through the use of standardized bleeding assessment tools, allowing for the quantification of bleeding symptoms as well as through clinical gestalt and judgment. The first-line laboratory workup must include a minimum set of hemostasis assays with normal results, including complete blood count, prothrombin time, activated partial thromboplastin time, thrombin time, fibrinogen, von Willebrand disease testing, factor VIII, platelet aggregation testing, and, if available, platelet-dense granule assessment. Following normal results of initial laboratory testing, specialized tests may be ordered based on examination in addition to the patient's clinical history, including measurement of individual clotting factor assays to identify other rare bleeding causes, and in rarer cases, additional platelet assays and fibrinolysis assays may be performed. Genetic testing involving targeted genomic sequencing of known genes associated with bleeding and platelet dysfunction is not currently part of the standard line of care, primarily due to the cost and low diagnostic yield.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".