Burkitt Lymphoma International Prognostic Index
Bibliographic record
Abstract
PURPOSE: Burkitt lymphoma (BL) has unique biology and clinical course but lacks a standardized prognostic model. We developed and validated a novel prognostic index specific for BL to aid risk stratification, interpretation of clinical trials, and targeted development of novel treatment approaches. METHODS: We derived the BL International Prognostic Index (BL-IPI) from a real-world data set of adult patients with BL treated with immunochemotherapy in the United States between 2009 and 2018, identifying candidate variables that showed the strongest prognostic association with progression-free survival (PFS). The index was validated in an external data set of patients treated in Europe, Canada, and Australia between 2004 and 2019. RESULTS: In the derivation cohort of 633 patients with BL, age ≥ 40 years, performance status ≥ 2, serum lactate dehydrogenase > 3× upper limit of normal, and CNS involvement were selected as equally weighted factors with an independent prognostic value. The resulting BL-IPI identified groups with low (zero risk factors, 18% of patients), intermediate (one factor, 36% of patients), and high risk (≥ 2 factors, 46% of patients) with 3-year PFS estimates of 92%, 72%, and 53%, respectively, and 3-year overall survival estimates of 96%, 76%, and 59%, respectively. The index discriminated outcomes regardless of HIV status, stage, or first-line chemotherapy regimen. Patient characteristics, relative size of the BL-IPI groupings, and outcome discrimination were consistent in the validation cohort of 457 patients, with 3-year PFS estimates of 96%, 82%, and 63% for low-, intermediate-, and high-risk BL-IPI, respectively. CONCLUSION: The BL-IPI provides robust discrimination of survival in adult BL, suitable for use as prognostication and stratification in trials. The high-risk group has suboptimal outcomes with standard therapy and should be considered for innovative treatment approaches.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".