Gemtuzumab Ozogamicin Combined With Intensive Chemotherapy in Patients With Acute Myeloid Leukemia Relapsing After Allogenic Stem Cell Transplantation
Bibliographic record
Abstract
BACKGROUND: More than one-third of patients with acute myeloid leukemia (AML) will relapse after allogenic hematopoietic cell transplant (allo-HCT). The main challenge is to overcome disease resistance to achieve a new complete remission while avoiding excessive toxicity. Gemtuzumab ozogamicin (GO), a conjugate of calicheamicin linked to the humanized monoclonal anti-CD33 antibody, has been used for refractory or relapsed AML with promising response rates, but liver toxicity of GO has long been considered a limiting factor. PATIENTS AND METHODS: We included 18 consecutive patients with AML relapsing after a first allo-HCT and treated with fractioned GO (fGO) and intensive chemotherapy. The median age was 40 years (range, 18-65). RESULTS: The overall response rate was 72% (13/18), including 7 complete remissions. No death was attributed to treatment toxicity. The main liver toxicity was transient and consisted of transaminase level elevation and hyperbilirubinemia. No cases of veno-occlusive disease were observed after the GO treatment. From the time of salvage treatment initiation, 1- and 2-year OS rates were 54% (95% confidence interval, 28%-74%) and 42% (95% confidence interval, 19%-63%), respectively. CONCLUSIONS: Our study suggests the feasibility, efficacy, and safety of an fGO-based salvage regimen combined with intensive chemotherapy in patients with CD33+ AML in the case of early relapse after an allo-HCT.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".