Is There (Still) a Place for Sequential Conditioning?
Bibliographic record
Abstract
There is still an unmet need for the treatment of high-risk hematological malignancies. To date, allogeneic stem cell transplantation remains the only chance of cure. Most patients suffering from high-risk hematological malignancies are of an older age and often present with comorbidities. Moreover, patients achieving remission often suffer from early relapse. Amongst the different treatment options, sequential conditioning has yet to prove its value against other conditioning regimens. Sequential conditioning relies on a short course of intensive chemotherapy that is quickly followed by immunosuppressive conditioning before allogeneic stem cell transplantation. Here, we will try to determine which patients can benefit from sequential conditioning. Amongst the different sequential regimens, we will also try to assess if one regimen is better than all the others. Despite the several studies conducted on sequential conditioning, very few are prospective work and head-to-head comparisons are almost inexistant. Sequential conditioning also relies on the use of prophylactic donor lymphocyte infusion post-transplantation. Hence, limiting non-relapse complications is of primary importance to the allow administration of post-transplant treatment. In the era of new targeting therapies, is there still a place for sequential conditioning? Can patients benefit from an association of new therapeutic agents and sequential conditioning?
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".