Effectiveness and safety of thiotepa as conditioning treatment prior to stem cell transplant in patients with central nervous system lymphoma
Bibliographic record
Abstract
BACKGROUND: Thiothepa is a cytostatic agent used in managing solid malignancies, and also as conditioning treatment before hematopoietic stem cell transplantation [HSCT]. This systematic review summarizes evidence on its effectiveness and safety, in patients with central nervous system [CNS] lymphoma. METHODS: We searched 3 databases for clinical studies. When feasible, we performed meta-analyses. RESULTS: We identified 13 eligible studies, none of which with a priori controls. So data synthesis focused on the 226 patients who received thiotepa. Based on pooled estimates, 75.9% of thiotepa-treated patients achieved a complete remission (95% confidence interval [CI] = 67.5-82.8), and 61.7% had a progression-free survival for up to 125 months post-treatment (95% CI = 49.4-72.7). However, 25.5% relapsed, 24.6% experienced infection, and 13.2% experienced neurotoxicity. DISCUSSION: Thiotepa-based conditioning followed by HSCT may be effective in most CNS lymphoma patients, with a manageable toxicity profile. But adequately powered randomized trials are needed to better evaluate and isolate the effects of thiotepa.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".