Secondary CNS relapse in diffuse large B-cell lymphoma: defining high-risk patients and optimization of prophylaxis strategies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite improvement in survival in diffuse large B-cell lymphoma (DLBCL) with the introduction of rituximab, central nervous system (CNS) relapse continues to represent a clinical challenge. A number of studies have evaluated clinical risk factors in an attempt to identify high-risk patients to direct CNS staging investigations and consider prophylaxis strategies. The CNS International Prognostic Index is a robust and reproducible risk model that can identity patients at high risk of CNS relapse, but its specificity remains limited. Studies are emerging of biomarkers that predict CNS relapse that can be integrated with clinical risk models to better identify high-risk patients for CNS-directed prophylaxis strategies. Because CNS parenchymal disease is the predominant compartment, prophylaxis should include deeply penetrant drugs such as high-dose methotrexate. However, this has been associated with toxicity and has limited use in older patients. Novel therapies are being tested in primary CNS lymphoma with encouraging results and may represent rational strategies to be further explored in the prophylaxis setting.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it