Polatuzumab vedotin in previously untreated DLBCL: an Asia subpopulation analysis from the phase 3 POLARIX trial
Bibliographic record
Abstract
In the phase 3 POLARIX study in previously untreated diffuse large B-cell lymphoma, polatuzumab vedotin combined with rituximab plus cyclophosphamide, doxorubicin, and prednisone (Pola-R-CHP) significantly improved progression-free survival (PFS) compared with rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) with similar safety. Patients were randomized 1:1 to 6 cycles of Pola-R-CHP or R-CHOP plus 2 cycles of rituximab alone. For registration of POLARIX in China, consistency of PFS in an Asia subpopulation (defined as ≥50% of the risk reduction in PFS expected in the global population) was evaluated. Overall, 281 patients were analyzed: 160 patients from Asia in the intention-to-treat (ITT) population of the global study and 121 from an ITT China extension cohort. Of these, 141 were randomized to Pola-R-CHP and 140 to R-CHOP. At data cutoff (28 June 2021; median follow-up 24.2 months), PFS met the consistency definition with the global population, and was superior with Pola-R-CHP vs R-CHOP (hazard ratio, 0.64; 95% confidence interval [CI], 0.40-1.03). Two-year PFS was 74.2% (95% CI, 65.7-82.7) and 66.5% (95% CI, 57.3-75.6) with Pola-R-CHP and R-CHOP, respectively. Safety was comparable between Pola-R-CHP and R-CHOP, including rates of grade 3 to 4 adverse events (AEs; 72.9% vs 66.2%, respectively), serious AEs (32.9% vs 32.4%), grade 5 AEs (1.4% vs 0.7%), AEs leading to study treatment discontinuation (5.0% vs 7.2%), and any-grade peripheral neuropathy (44.3% vs 50.4%). These findings demonstrate consistent efficacy and safety of Pola-R-CHP vs R-CHOP in the Asia and global populations in POLARIX. This trial was registered at https://clinicaltrials.gov/ct2/home as # NCT03274492.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".