Can we move beyond myeloablative conditioning ( <scp>MAC</scp> )? A comparison of <scp>MAC</scp> versus reduced intensity conditioning ( <scp>RIC</scp> ) in patients aged younger than 65 years undergoing allogeneic haematopoietic cell transplantation using <scp>ATG</scp> ‐ <scp>PTCy</scp> ‐ <scp>CSA</scp> for <scp>GVHD</scp> prophylaxis
Bibliographic record
Abstract
Allogeneic haematopoietic cell transplantation (HCT) offers a curative option for numerous haematological disorders; however, its myeloablative conditioning (MAC) regimens are associated with substantial toxicity. Reduced intensity conditioning (RIC) regimens were developed to mitigate transplant-related toxicity and broaden eligibility-particularly for older or medically unfit patients-though their use in younger, fit patients remains debated. In this retrospective study, we compared outcomes between MAC and RIC in patients aged younger than 65 years undergoing allogeneic HCT with a unified graft-versus-host disease (GVHD) prophylaxis regimen comprising anti-thymocyte globulin (ATG), post-transplant cyclophosphamide (PTCy) and ciclosporin (CsA). Propensity score matching was applied to reduce confounding. At 2 years post-transplant, there were no statistically significant differences in overall survival (OS) between the groups (MAC: 68.6% vs. RIC: 65.9%; p = 0.61) or in non-relapse mortality (NRM) (MAC: 15.8% vs. RIC: 12.5%; p = 0.26). However, relapse incidence was significantly higher in the RIC group (27.0%) than in the MAC group (16.1%; p = 0.01). These findings reinforce the continued relevance of MAC in younger patients who are candidates for intensive therapy, as it appears to offer superior disease control without a concomitant increase in NRM. Prospective studies are warranted to further delineate the role of conditioning intensity in the context of contemporary GVHD prophylaxis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".