An illustrated review of bleeding assessment tools and common coagulation tests
Bibliographic record
Abstract
Recognizing the complexity of coagulation tests and currently used anticoagulants, we developed this illustrated review on bleeding assessment tools and common coagulation screening tests. Quantitative bleeding assessment tools (BATs) are available to standardize the bleeding history and improve the pretest probability prior to coagulation testing. We describe use of BATs and the principles, indications, and limitations of the prothrombin time (PT)/International Normalized Ratio, activated partial thromboplastin time (APTT), and 50:50 mix. Use of these tests to identify coagulation factor deficiencies, specific and nonspecific inhibitors, coagulopathy of liver disease, disseminated intravascular coagulation, and commonly used anticoagulant medications are reviewed. Current literature suggests that unnecessary coagulation testing is rampant. The PT and APTT have astoundingly low sensitivity (1.0%-2.1%) for detection of clinically significant bleeding disorders. Thus, current guidelines recommend against the use of screening PT and APTT in preoperative patients undergoing noncardiac/vascular surgery.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".