The influence of immunodeficiency, disease features, and patient characteristics on survival in plasmablastic lymphoma
Bibliographic record
Abstract
ABSTRACT: Plasmablastic lymphoma (PBL) is a rare and aggressive non-Hodgkin lymphoma associated with immunodeficiency, characterized by uncertain treatment approaches and an unfavorable prognosis. We conducted a multicenter, international, retrospective cohort study, aiming to characterize the clinical features, risk factors, and outcomes of patients with PBL. Data were collected from 22 institutions across 4 countries regarding patients diagnosed with PBL between 1 January 1999 and 31 December 2020. Survival risk factors were analyzed using both univariate and multivariate regression models. Overall survival (OS) was calculated using Kaplan-Meier statistics. First-line treatment regimens were stratified into standard- and higher-intensity regimens, and based on whether they incorporated a proteasome inhibitor (PI). A total of 281 patients (median age, 55 years) were included. Immunodeficiency of any kind was identified in 144 patients (51%), and 99 patients (35%) had HIV-positive results. The 5-year OS for the entire cohort was 36% (95% confidence interval, 30%-42%). In multivariate analysis, inferior OS was associated with Epstein-Barr virus-negative lymphoma, poor performance status, advanced stage, and bone marrow involvement. In an independent univariate analysis, the international prognostic index was associated with OS outcomes. Neither immunosuppression nor HIV infection, specifically, influenced OS. Among patients treated with curative intent (n = 234), the overall response rate was 72%. Neither the intensity of the treatment regimen nor the inclusion of PIs in first-line therapy was associated with OS. In this large retrospective study of patients with PBL, we identified novel risk factors for survival. PBL remains a challenging disease with poor long-term outcomes.
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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 | 0.003 |
| 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".