Central nervous system lymphomas—Assessment and treatment and prevention of central nervous system relapse
Bibliographic record
Abstract
In this review focused on lymphoma and the central nervous system (CNS), we summarize recent developments in the management of primary (PCNSL) and secondary CNS lymphoma (SCNSL), treatment of CNS lymphoma in the older population, the neuroradiological assessment of CNS lymphoma and finally highlight the ongoing debate on optimal CNS prophylaxis. The section on PCNSL focuses on the different approaches available for frontline treatment in Europe and the United States and discusses consolidation strategies. We then highlight available strategies to treat PCNSL in the elderly population, an area of unmet need. New therapies aiming at minimizing toxicity and prioritizing quality of life are emerging for these patients. Secondary CNS lymphoma, especially in the relapsed/refractory setting is another area of unmet need, and the efficacy of CAR-T cell therapy is being explored. We provide an overview of the imaging challenges in the neuroradiological assessment of CNS lymphoma. Finally, the section on CNS prophylaxis summarizes recent findings from large retrospective studies challenging the efficacy of present approaches to prophylaxis in higher-risk patients with lymphoma.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".