Low Rate of Central Nervous System Relapse of Diffuse Large B-Cell Lymphoma Despite Limited Use of Intrathecal Prophylaxis
Bibliographic record
Abstract
Background: The incidence of central nervous system (CNS) relapse in diffuse large B-cell lymphoma (DLBCL) varies, and the optimum strategy of CNS prophylaxis remains to be defined. We aimed to evaluate the incidence of CNS relapse in DLBCL patients and the role of CNS prophylaxis. Methods: Data on patients diagnosed with DLBCL at our institution from January 2011 to June 2019 were retrospectively collected from the charts and computerized hospital information system for patient demographics, lymphoma stage at diagnosis, CNS international prognostic index (IPI) scores, extra-nodal sites, chemotherapy type, CNS prophylaxis, and CNS relapse. CNS prophylaxis comprised intrathecal (IT) chemotherapy and was administered based on the presence of high-risk features. Patients with primary CNS lymphoma and CNS involvement at diagnosis were excluded. Results: Of 101 patients, 58 (57.5%) were males with a median age of 56 (range: 16 - 87) years. Ann Arbor stages of I - IV were confirmed in nine, 21, 17, and 50 patients, respectively. The lung was the most common extranodal site involved (27, 26.7%). Twenty-five (24.75%) patients had a high-risk CNS-IPI score. Ninety-three percent of patients received R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) chemotherapy. Sixteen patients received CNS prophylaxis as IT methotrexate (± cytarabine and hydrocortisone). Despite high-risk CNS-IPI scores, nine (36%) patients did not receive CNS prophylaxis. After a median follow-up of 36 (range: 4 - 114) months, two patients with high-risk CNS-IPI score developed CNS relapse and died shortly. Conclusions: CNS relapse of DLBCL was uncommon in this patient population. Low incidence of CNS relapse despite limited use of IT prophylaxis may suggest adequacy of IT prophylaxis in these patients.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".