Upfront double high-dose chemotherapy with DICEP followed by BEAM and autologous stem cell transplantation for poor-prognosis aggressive non-Hodgkin lymphoma
Bibliographic record
Abstract
A single center, prospective clinical trial was conducted evaluating 2 cycles of induction high-dose chemotherapy for adults younger than 65 years of age with aggressive non-Hodgkin lymphoma (NHL) and 2 to 3 Age-Adjusted International Prognostic Index risk factors. Patients received one cycle of standard dose cyclophosphamide, doxorubicin, vincristine, and prednisone (CHOP) followed by one cycle of dose-intensive cyclophosphamide 5.25 g/m(2), etoposide 1.05 g/m(2), cisplatin 105 mg/m(2) (DICEP), then underwent autologous blood stem cell collection, followed by one cycle of high-dose carmustine (BCNU) 300 mg/m(2), etoposide 800 mg/m(2), Ara-C 1600 mg/m(2), melphalan 140 mg/m(2) (BEAM), and autologous stem cell transplantation (ASCT) and radiotherapy to prior bulk. From June 1998 to August 2004, 55 patients aged 20 to 63 years (median 44 years) were accrued, 51 (92%) of whom had diffuse large B-cell NHL. Poor prognostic factors included stage 4 (n = 46), elevated lactate dehydrogenase (LDH; n = 47), Eastern Cooperative Oncology Group (ECOG) performance status 2 to 4 (n = 43), bulky mass more than 10 cm (n = 34), and marrow involvement (n = 16). Only one patient experienced nonrelapse mortality. With a median follow-up of 49 months, 4-year event-free survival (EFS) and overall survival (OS) rates for all 55 patients are 72% (95% confidence interval [CI] = 60%-84%) and 79% (95% CI = 69%-90%), respectively. In conclusion, CHOP-DICEP-BEAM is feasible and gave encouraging EFS and OS for patients with poor-prognosis aggressive NHL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".