RESPONSE: Re: Consolidation Therapy With Autologous Bone Marrow Transplantation in Adults With Acute Myeloid Leukemia: A Meta-analysis
Bibliographic record
Abstract
In their letter to the Journal, as well as in their meta-analysis ( 1 ) , Levi et al. contend that a decline in the treatment-related mortality associated with the use of peripheral blood stem cells instead of bone marrow for autologous transplantation supports the routine use of autologous stem cell transplantation (ASCT) in adults with acute myeloid leukemia in first remission. Although we agree that this strategy warrants further study in a prospective, randomized clinical trial, we have several concerns about the assumptions that the authors have made in order to reach their conclusions. First, the simulations presented by Levi et al. assume that improvements in supportive care and the development of safer transplant techniques have decreased treatment-related mortality among patients who receive an ASCT. The simulations do not account for improvements in survival as a result of better supportive care in patients who receive chemotherapy alone. Such a decrease in treatment-related mortality over time, for example, was observed among children who were treated with chemotherapy alone on the Tenth Medical Research Council Acute Myeloid Leukaemia Trial (MRC AML 10) ( 2 ). The authors’ supposition that treatment-related mortality has decreased only among transplant recipients biases their analysis in favor of ASCT. In our meta-analysis, we noted that the pooled treatment-related mortality among patients who were randomly assigned to receive chemotherapy (or no further treatment) was 4.4% ( 3 ). The authors’ assumption of a 3% treatment-related mortality in the ASCT arm of their second simulation suggests that ASCT results in a lower risk of treatment-related mortality than chemotherapy alone—a contention that is not supported by the available literature. Second, the authors’ assertion that the treatment-related mortality among patients receiving ASCT is currently 0%–6% is based on data from patients who actually received ASCT ( 4 ). However, such an analysis by treatment received is biased in favor of ASCT. Patients who actually undergo transplantation will have better outcomes when compared with all patients randomly assigned to receive transplantation because some of the latter patients will relapse or die before receiving the procedure or will be too ill to proceed to transplantation. Therefore, an intent-to-treat analysis is more appropriate for comparing transplantation with chemotherapy than an analysis by treatment received. Both the original meta-analysis published by Levi et al. ( 1 ) and our meta-analysis ( 3 ) based calculations of survival on intent-to-treat analyses. In the absence of data from a randomized clinical trial, it is impossible to know whether a decrease in mortality among patients who receive peripheral blood stem cells is attributable to a true reduction in treatment-related mortality or, at least in part, to the use of an analysis of only patients who received the treatment. Third, the formula used by Levi et al. to calculate the expected deaths in the ASCT arm is unconventional. We urge the authors to clarify their calculations and provide a reference for their approach. Although we agree that the role of ASCT among patients in first remission of acute myeloid leukemia warrants further study, we caution against drawing conclusions by combining the results of previous randomized studies of autologous bone marrow transplantation with estimates of survival of ASCT derived from current single-arm studies.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.042 | 0.010 |
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".