Hairy Cell Leukemia With Splenic Rupture: Hematological Changes and Disappearance of Hairy Morphology After Splenectomy
Bibliographic record
Abstract
We present the case of a 64-year-old man who suffered a splenic rupture following a fall. A peripheral blood smear showed mononuclear cells with “hairy” cytoplasmic projections. A computed tomography (CT) angiogram revealed splenomegaly with a hematoma and active contrast extravasation. He underwent coil embolization of the splenic artery but subsequently developed worsening abdominal pain and ileus, requiring a splenectomy. Pathological examination of the spleen showed extensive infiltration by hairy cell leukemia (HCL). Bone marrow biopsy revealed hypercellular marrow predominantly infiltrated by HCL cells positive for CD20, CD103, and the BRAF V600E mutation. After the splenectomy, pancytopenia gradually improved. The symptoms of the patient, such as fatigue, weight loss, and night sweats, were also resolved. Circulating HCL cells were significantly reduced, and the “hairy” morphology became smoother. To our knowledge, this morphologic change of the hairy cells after splenectomy has not been reported in the past. Its mechanism is not known. We postulate that splenectomy may induce molecular or immunological changes that alter HCL cell behavior, which warrants further research.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".