Anticoagulation in thrombocytopenic patients – Time to rethink?
Bibliographic record
Abstract
One of the difficult clinical situations in the anticoagulation era is how to give these medications to patients with significantly reduced platelet counts. The concern is the heightened bleeding risk, and the current practice is to apply a certain platelet count threshold below which the use of anticoagulant is deemed unsafe. However, this is not an evidence-based approach especially because the thresholds arose from studies in patients with acute leukemia. In this forum article, we discuss the bleeding risk estimation in thrombocytopenic patients when the decreased counts may not be related to marrow underproduction and aim to identify possible markers which can help in this risk estimation beyond platelet counts. We exhort future studies to include a combination of these markers, which may then guide us to administer safe anticoagulation in patients with severe thrombocytopenia.
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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 | 0.005 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.058 | 0.047 |
| Insufficient payload (model declined to judge) | 0.008 | 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".