Outcomes of Autologous Stem Cell Transplantation for Non-Hodgkin Lymphoma Patients at a Tertiary Referral Centre
Bibliographic record
Abstract
Background: Autologous stem cell transplant (ASCT) has been used as a consolidative treatment modality in non-Hodgkin’s lymphoma (NHL), but its role in NHL management is still evolving. The study aimed to evaluate the patient outcomes based on age, NHL subtypes, and conditioning regimen. Method: We performed a retrospective analysis of NHL patients who received ASCT (n = 140) in our centre from 1992-2015. Data were gathered for this investigation using electronic records and case notes. Refractory illness, relapse, progressive disease, or death were all considered progression events. Time from ASCT to the last follow-up or progression event was used to define progression-free survival (PFS), and time from ASCT to death or the final follow-up was used to define overall survival (OS). Results: Median age at ASCT was 55 years (16-68). Amongst patients ≤60 years (n = 109) and >60 years (n = 31), there was no significant difference in PFS (P = 0.756), OS (P = 0.711), neutrophil (12.5 vs. 11 days) and platelet (12 vs. 14 days) engraftment times. Amongst follicular lymphoma patients (n = 54) who received BEAM (carmustine, etoposide, cytarabine, melphalan) (n = 30) or Cy/TBI (cyclophosphamide/ total body irradiation) (n=24) conditioning, there was no significant difference between PFS (P = 0.111) and OS (P = 0.667). There was no significant difference (P = 0.46) in the incidence of second malignancies in the patient receiving BEAM or TBI-based conditioning. Conclusion: ASCT can be safely performed for NHL in patients >60 years with outcomes similar to those ≤60 years. TBI based conditioning appear safe with similar outcomes to BEAM in follicular lymphoma patients. Prospective studies are needed to confirm these findings.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".