Infections and association with different intensity of chemotherapy in children with acute myeloid leukemia
Bibliographic record
Abstract
BACKGROUND: The objectives were to compare infections during different intensities of therapy in children with acute myeloid leukemia (AML). METHODS: Subjects were children enrolled in Children's Cancer Group 2891 with AML. In phase 1 (induction), patients were randomized to intensive or standard timing. In phase 2 (consolidation), those with a family donor were allocated allogeneic stem cell transplantation (SCT); the remainder were randomized to autologous SCT or chemotherapy. This report compares infections between different treatments on an intent-to-treat basis. RESULTS: During phase 1, intensive timing was associated with more bacterial (57.7% vs 39.4%; P < .001), fungal (27.4% vs 9.9%; P < .001), and viral (14.0% vs 3.9%; P < .001) infections compared with standard timing. During phase 2, chemotherapy was associated with more bacterial (56.5% vs 40.1%; P = .005), but similar fungal (9.5% vs 6.1%; P = 1.000) and viral (4.2% vs 12.9%; P = .728) infections compared with allogeneic SCT. No differences between chemotherapy and autologous SCT infections were seen. Fatal infections were more common during intensive compared with standard timing induction (5.5% vs 0.9%; P = .004). Infectious deaths were similar between chemotherapy, autologous SCT, and allogeneic SCT. CONCLUSIONS: Prevalence of infection varies depending on the intensity and type of treatment. This information sheds insight into the mechanisms behind susceptibility and outcome of infections in pediatric AML.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".