Cyclic Thrombocytopenia in the Setting of Intracranial Hemorrhage: A Diagnostic and Therapeutic Challenge
Bibliographic record
Abstract
Cyclic thrombocytopenia (CTP) as the name suggests presents with cyclic episodes of thrombocytopenia and is frequently initially misdiagnosed as immune thrombocytopenia. Following a lack of sustained response or abnormally increased response to common treatments used for immune thrombocytopenia, a proper diagnosis of CTP can then be made. Prior reports have shown a subset of patients who respond to cyclosporin A. Here, we present a case of CTP that was initially at another facility presumed to have and treated for immune thrombocytopenic purpura. However, after multiple attempts to treat with steroids, intravenous immunoglobulin (IVIG), rituximab, and eltrombopag, episodes of severe thrombocytopenia followed by thrombocytosis continued. The patient ultimately developed intracerebral hemorrhage (ICH) in the setting of one of the episodes of severe thrombocytopenia and developed multiple subsequent complications from which the patient unfortunately did not recover. It was only after developing ICH that the patient had been evaluated at a center with hematology consultation capabilities, at which time after a detailed review of his case and pattern recognition the proper diagnosis of CTP was made with initiation of cyclosporine. This case was further complicated by need to maintain an adequate platelet threshold post-ventriculoperitoneal shunt placement which was necessary due to his ICH and was placed before diagnosis of CTP could be made. While CTP is a rare diagnosis, this case reinforces a greater need to properly diagnose and consider cyclosporine treatment for CTP, as it has been effective in some patients and may help to prevent patient morbidity and especially catastrophic bleeding 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| 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".