The Clinical Conundrum of Managing Ischemic Stroke in Patients with Immune Thrombocytopenia
Bibliographic record
Abstract
Guidelines are lacking for management of acute ischemic stroke and stroke prevention in patients with immune thrombocytopenia (ITP). Our aim is to highlight the dilemma inherent in managing patients with both significant bleeding and thrombotic risk factors. In this review, we present two patients with history of ITP who presented with acute ischemic stroke and received tissue plasminogen activator (tPA) and endovascular thrombectomy (EVT), a rare management strategy in this patient population. In addition, we identified 27 case reports of ischemic stroke in patients with ITP; none of them received tPA or EVT. Furthermore, there are 92 patients with significant thrombocytopenia with no available data regarding the cause of thrombocytopenia, who were acutely treated with tPA or EVT. Conclusive evidence cannot be determined based on these limited number of cases. Future multicenter prospective cohort studies in patients with ITP are needed to provide better evidence-based treatment plans. At present, treatment of acute ischemic stroke in patients with ITP requires close collaboration between hematology and vascular neurology experts to find a balance between the benefit and risk of hemorrhagic complications.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".