Laboratory issues in bleeding disorders
Bibliographic record
Abstract
The clinical history of the patient and of his/her relatives is the most important tool for making correct diagnosis of inherited or acquired bleeding disorders. Several attempts have been made by clinicians to evaluate the sensitivity and specificity of bleeding symptoms. Specific and detailed questionnaires have been designed to quantify the bleeding tendency of patients with von Willebrand's disease (VWD) and a bleeding score has been calculated. VWD is considered the most frequent inherited bleeding disorder according to population studies: however, due to the complexity of its diagnosis, the number of patients with correct diagnosis of VWD in many developing countries is relatively low and most cases remain still under- or misdiagnosed. Once bleeding history is carefully evaluated by means of a bleeding score, the laboratory workout should be organized to find out the specific defect of haemostasis responsible for bleeding. Since factors involved in haemostasis are many, the correct approach must include first level screening tests with the aim to identify the abnormal phase of haemostasis involved: then, second level tests should be focused on the specific factors within the abnormal step of haemostasis. Among many other acquired bleeding disorders related to clinical conditions or to the use of drugs, the acquired inhibitors of haemostasis are rare but should be immediately characterized by appropriate laboratory tests because they can be often life-threatening for the patients.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".