How to Diagnose and Treat CD5-Positive Lymphomas Involving the Spleen
Bibliographic record
Abstract
Patients with CD5-expressing lymphomas presenting with splenomegaly are frequently diagnosed with chronic lymphocytic leukemia. The most important differential diagnosis is mantle cell lymphoma, both in its classical and leukemic, non-nodal forms, given its prognostic and therapeutic implications. Other small B-cell neoplasms that frequently involve the spleen and occasionally express CD5 include the splenic marginal zone lymphoma, hairy cell leukemia and, rarely, lymphoplasmacytic lymphoma. The frequency of CD5 positivity depends in part on the sensitivity of the detection methods employed. Usually, a combination of morphological, immunophenotypic and molecular findings allows for a precise sub-classification of CD5-positive, low-grade B-cell lymphomas of the spleen. Some of these tumors may display a mixture of small and larger B cells, raising the possibility of more aggressive lymphomas, such as diffuse large B-cell lymphomas (DLBCL). Approximately 5-10% of DLBCL are CD5-positive and some may manifest as primary splenic lesions. When available, the morphology of DLBCL in the splenic tissue is distinctive and a leukemic picture is very rare. In conclusion, the appropriate morphological and clinical context assisted by flow cytometry panels and/or immunohistochemistry allows the differential diagnosis of CD5-positive, non-Hodgkin, B-cell lymphomas involving the spleen.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".