Prognostic Factors and Treatment Outcome of Relapsing and Refractory Pediatric Mature B-cell Non-Hodgkin Lymphoma, Children’s Cancer Hospital Egypt Experience
Bibliographic record
Abstract
BACKGROUND: Relapsed non-Hodgkin lymphoma treated by chemotherapy and hematopoietic stem cell transplantation (HSCT) has a dismal prognosis. PATIENTS: It is a retrospective study, including pediatric patients diagnosed as mature B-cell non-Hodgkin lymphoma who were primarily refractory or relapsed over 8 years at CCHE. The aim of the study was to analyze the prognostic factors and outcomes of this group of patients. Our result is, 53 of 750 (7%) patients were included. Thirty-four (64.2%) patients had Burkitt lymphoma. Forty-eight (90.6%) patients received LMB 96 protocol initially. The median delay of duration between chemotherapy cycles in first-line treatment was 37 days. Thirty-five (66%) patients relapsed, 23 (65.7%) of them relapsed early, whereas 18 (34%) had tumor progression. Thirty-one (58.5%) patients presented with stage IV at the time of relapse. rituximab, ifosfamide, carboplatin, etoposide was the second line of treatment in 42 (79.24%) patients, and complete second remission was achieved only in 13 (24.3%) patients. Allogeneic HSCT was done for 4 (7.5%) patients, and auto HSCT was done for 3 (5.7%) patients. Three years of overall survival for relapsed and progressed patients were 35.3% and 11.1%, respectively, with a P-value of 0.009. Three years overall survival for patients who underwent HSCT was 85.7% compared with 18.1% for no HSCT with a P-value of 0.007. CONCLUSIONS: The relapse rate is higher than literatures because of the delay of duration between chemotherapy cycles in first-line treatment and more advanced stage at time of relapse. Progression of the disease had a worse outcome than relapse. HSCT in patients with the second remission markedly improved the outcome.
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 | 0.000 | 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".