Myeloablative Allogeneic Stem Cell Transplantation for Relapsed Composite Low and Intermediate Grade Lymphoma Is Not Superior to Autologous Stem Cell Transplantation - Reduced Relapse Risk Is Offset by Higher Treatment Related Mortality.
Bibliographic record
Abstract
Abstract No reports compare autologous and allogeneic stem cell transplantation (auto-SCT vs allo-SCT) for patients (pts) with relapsed (REL) composite low and intermediate grade non-Hodgkin lymphoma (L/I-NHL). Fifty four pts with composite L/I-NHL (30 transformed, 16 composite(same site), 8 discordant(different sites)) who underwent allo (n=40) or auto-SCT (n=14) from Jan ‘89 to June ‘05 were evaluated (Table 1) to compare long-term results. Twenty pts (37%) (11/40 allo and 9/14 auto) are alive with median follow-up of 32 months (range 10–87). The 2 and 5-y OS for the whole group were 43% [95% confidence interval: 28–56%] and 29% [14–46%] respectively; the 2 and 5-y EFS 40% [26–53%] and 24% [11–39 %]. 5-y OS for the auto and allo-SCT were 51% and 23% respectively (p=.09)(Fig. 1). The 2 and 5-y EFS for the allo vs auto-SCT groups were 36% [24–55%] vs 54% [33–89%] and 27% vs 23% (p=0.4). Thirty four pts (63%) died; 18 (33%) with REL NHL and 16 (30%) with TRM. TRM was due to GVHD (n=7), infection (n=6), cerebral edema (n=1) and veno-occlusive disease (n=1). All but one (cerebral edema) TRM deaths occurred post allo-SCT. The cumulative incidence (CI) of TRM and REL for the whole group were 37% [17–57%] and 47% [30–64%]. The CI of TRM for allo and auto-SCT were 47% [20–74] and 8% [0–23%] respectively (p=.009)(Fig. 2). Twenty pts (37%) relapsed post SCT (14/40, 35% allo and 6/14, 43% auto). CI of REL NHL for allo and auto-SCT were 42% [23–61%] and 65% (p=.008)(Fig. 2). In conclusion EFS for composite L/I-NHL is low post auto-SCT due to a high relapse rate despite acceptable TRM. While relapse risk is significantly lower post allo-SCT, this advantage is offset by a higher TRM in these pts. Future attempts to reduce TRM while preserving the allogeneic graft vs lymphoma effect may be beneficial. Table 1. The clinicopathological characteristics of 54 patients treated with auto and allo-SCT for relapsed composite L/I-NHL Parameter Allo-SCT n=40(%) Auto-SCT n=14(%) * at diagnosis Age: Range (Median) 28–57(44) 25–59(49) M:F 2:1 1.3:1 Diagnosis Transformed lymph. 25(63) 5(36) Discordant lymph. 7(17) 1(7) Composite lymph. 8(20) 8(57) Initial Stage I/II 4(10) 6(42) III/IV 36(90) 8(58) B symptoms* 11(28) 6(43) BM involvement* 29(73) 3(21) IPI* 0–1 18(45) 11(79) 2–3 21(53) 3(21) 4–5 1(2) 0 Prior treatment CHOP 33(83) 14(100) Purine analogue 17(43) 2(14) Rituximab 12(30) 6(43) Fig 1: OS for auto/alle groups Fig 1:. OS for auto/alle groups Figure Figure
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".