A multiagent strategy to decrease regimen-related toxicity in children undergoing allogeneic hematopoietic stem cell transplantation
Bibliographic record
Abstract
Regimen-related toxicity (RRT) is a frequent cause of morbidity and mortality after allogeneic hematopoietic stem cell transplantation (HSCT). In this pilot study, we examined the feasibility and potential efficacy of administering a fixed combination of agents as a novel approach to reducing RRT in children undergoing HSCT. Thirty-seven patients were treated with ursodeoxycholic acid, folinic acid, vitamin E, and parenteral nutrition titrated to measured energy expenditure in the peritransplantation period. Outcomes were compared with those in historical controls (n = 131). Compliance with oral ursodeoxycholic acid and vitamin E of at least 90% was achieved in a mean of 86% (95% confidence interval, 75%-97%) of patients. In the study group, we observed (1) reduced prevalence and severity of mucositis (P = .008 and.004, respectively); (2) less severe hepatic toxicity (P = .007); and (3) shorter time to engraftment (P = .02) compared with the control group. These benefits appeared most pronounced among high-risk patients. The administration of this regimen, including oral medications, is feasible during the peritransplantation period, and it is well tolerated. The decreased RRT observed in comparison to historical controls suggests that combination approaches deserve exploration as a means of reducing the morbidity of HSCT.
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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) | 0.000 | 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".