Management of a Patient With Subdural Hematoma Complicated by the Presence of Heparin-Induced and Suspected Concomitant Immune Thrombocytopenia: A Case Report
Bibliographic record
Abstract
A patient with antiphospholipid syndrome treated with warfarin presented to the emergency department with a traumatic subdural hematoma. Two days following the evacuation of the hematoma, treatment with low molecular weight heparin (LMWH) was initiated. One week later, the patient developed new symptoms. The hematoma had relapsed and the platelet count had decreased considerably. The thrombocytopenia was diagnosed as heparin-induced and the anticoagulant treatment was discontinued. Following increase of the platelet count, treatment with fondaparinux was initiated. However, 1 day following that, the platelet count had decreased again and cross-reaction between the LMWH and fondaparinux was suspected. Due to the risk of hematoma relapse, the patient started treatment with cortisone, responding promptly. The platelet count was stabilized and the patient resumed the treatment with warfarin without complications. The thrombocytopenia was initially heparin-induced and complicated by the cross-reaction between LMWH and fondaparinux as well as the presence of suspected autoimmune thrombocytopenia, as suggested by the response to corticosteroids. This case report illustrates the difficulties in diagnosing and managing thrombocytopenia of complex etiology. J Hematol. 2015;4(3):202-204 doi: http://dx.doi.org/10.14740/jh219w
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".