Bibliographic record
Abstract
Abstract Introduction Laboratory investigations for bleeding disorders are warranted when an individual has a personal and/or family history of bleeding, and/or laboratory findings that suggest the possibility of an inherited or acquired bleeding disorder. Methods This review summarizes author's experience with ordering and reporting on diagnostic investigations for common and rare bleeding disorders, with consideration of recent articles on diagnosing bleeding disorders. An updated strategy is presented for investigating common and rare, congenital and acquired bleeding disorders. Results An investigation of a suspected bleeding disorder requires a practical strategy that considers the clinical problem to be investigated, the pretest probability of true‐positive and false‐positive findings, the investigations can be performed locally or in a reference laboratory and limit the number of blood samples required to establish a diagnosis. It is often advantageous to simultaneously test for von Willebrand disease and platelet function disorders, and for coagulation defects, including fibrinogen disorders. An investigation for rarer bleeding disorders, including those affecting factor XIII, α 2 antiplasmin, and plasminogen activator inhibitor‐1, is appropriate when faced with a severe congenital or acquired bleeding problem that cannot be explained by the initial diagnostic investigations. Conclusion An organized strategy for investigating bleeding disorders that consider important issues, confirms abnormal findings, encourages proper interpretation of the results, and provides a helpful framework for assessing both common and rare causes of bleeding.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.011 |
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".