Expert Approaches to Common Bleeding and Thrombotic Problems—Part I
Bibliographic record
Abstract
Welcome to this first of two special issues of Seminars in Thrombosis & Hemostasis devoted to how experts approach common bleeding and thrombotic problems. This issue features expert opinion on the diagnosis and management of common disorders, with discussion of the current evidence and the practical issues that need to be considered for management of thrombotic and bleeding problems. The topics covered span many of the common bleeding and thrombotic problems that result in a hematology referral and include discussion of special populations, such as children and neonates.[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] Where there are controversies, or a lack of evidence, the expert contributors to this issue have provided thoughtful advice, sometimes with new evidence.[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] All of the articles in this issue discuss clinical management and laboratory testing.[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] Currently, many common thrombotic and bleeding disorders can be evaluated by fairly simple investigations that are generally widely available (e.g., determination of the international normalized ratio [INR], D-dimer levels, and measurement of coagulation factor levels).[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] However, some investigations for common disorders, such as transmission electron microscopy to diagnose platelet dense-granule deficiency ([Fig. 1]), require an evaluation by expert centers.[14] Additionally, there are gaps in the clinical and laboratory assessment of common bleeding and thrombotic problems, as discussed by the contributors to this issue.[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] Taken together, the articles present helpful diagnostic and management reviews, with in-depth discussions of key issues and current evidence. The issue is anticipated to be highly valued by individuals engaged in clinical, laboratory, educational, and research practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".