The revised International Prognostic Index (R-IPI) is a better predictor of outcome than the standard IPI for patients with diffuse large B-cell lymphoma treated with R-CHOP
Bibliographic record
Abstract
Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous entity, with patients exhibiting a wide range of outcomes. The addition of rituximab to CHOP chemotherapy (R-CHOP)has led to a marked improvement in survival and has called into question the significance of previously recognized prognostic markers. Since randomized controlled trials of R-CHOP in DLBCL have included select subgroups of patients, the utility of the International Prognostic Index (IPI) has not been reassessed. We performed a retrospective analysis of patients with DLBCL treated with R-CHOP in the province of British Columbia to assess the value of the IPI in the era of immunochemotherapy. The IPI remains predictive, but it identifies only 2 risk groups. Redistribution of the IPI factors into a revised IPI (R-IPI) provides a more clinically useful prediction of outcome. The R-IPI identifies 3 distinct prognostic groups with a very good (4-year progression-free survival [PFS] 94%, overall survival [OS] 94%), good (4-year PFS 80%, OS 79%), and poor (4-year PFS 53%, OS 55%) outcome, respectively (P < .001). The IPI (or R-IPI) no longer identifies a risk group with less than a 50% chance of survival. In the era of R-CHOP treatment, the R-IPI is a clinically useful prognostic index that may help guide treatment planning and interpretation of clinical trials.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".