Oral mucositis after tacrolimus/sirolimus or cyclosporine/methotrexate as graft‐versus‐host disease prophylaxis
Bibliographic record
Abstract
OBJECTIVES: To determine whether treatment with tacrolimus plus sirolimus (Tac/Sir) as a prophylaxis for graft-versus-host disease worsens severe oral mucositis and delays healing compared to cyclosporine plus methotrexate (CsA/Mtx) following haematopoietic stem cell transplantation. SUBJECTS AND METHODS: The study comprised 141 patients: 73 randomized to receive Tac/Sir and 68 to receive CsA/Mtx. The oral mucositis assessment scale and toxicity grading according to WHO were used to assess the severity, peak and duration of oral mucositis from the day -3 to day 24 post-transplant. RESULTS: Eighty-seven patients developed oral mucositis in the first 24 days post-transplant. No significant difference in oral mucositis severity between the Tac/Sir and CsA/Mtx groups was observed. The peak oral mucositis score occurred on day 10 in both groups. Although oral mucositis scores had returned to baseline in the CsA/Mtx group on day 24 post-transplant, no significant difference compared with the Tac/Sir group was found. CONCLUSIONS: The introduction of tacrolimus/sirolimus as a graft-versus-host disease prophylaxis in haematopoietic stem cell transplantation increased neither the incidence nor severity of oral mucositis compared with cyclosporine/methotrexate. Furthermore, oral mucositis healing was not prolonged and followed the same time pattern as cyclosporine/methotrexate.
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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 | 0.001 |
| 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 | 0.000 |
| 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".