Risk Factors for Hepatotoxicity From L-Asparaginase Chemotherapy In Children With Acute Lymphoblastic Leukemia
Bibliographic record
Abstract
Abstract: \nIntroduction: L-asparaginase chemotherapy often causes hepatotoxicity and affects complete remission in pediatric acute lymphoblastic leukemia (ALL). This study aims to investigate the risk factors that affect the incidence of hepatotoxicity caused by L-asparaginase chemotherapy in ALL children. Methods: An observational study with prospective sampling was conducted at Dr. Soetomo Hospital, Surabaya. The inclusion criteria included ALL children aged 1-18 years, undergoing ALL Induction phase chemotherapy based on the 2018 Indonesian Children's ALL protocol as evidenced by bone marrow aspiration, receiving L-asparaginase chemotherapy, and obtaining written consent from parents or guardians. Each child had 3 ml of blood drawn from a peripheral vein to assess their complete blood count, alanine transaminase (ALT) levels, and albumin level. Results: Thirty-two children with ALL were collected. Two of them were excluded due to allergic reaction and enable to continue the L-asparaginase chemotherapy. Thirty of them were eligible participants. Approximately 53.3% of ALL children aged ≤ seven years. Fourteen (47%) children with ALL were included in the standard-risk group and 16 (53%) of them included high-risk group. There were significant differences in ALT levels between the four stages of observation (p=<0.001). Twenty-two ALL children had hepatotoxicity (73.3%), while 8 had non-hepatotoxicity (26.7%). Two risk factors had a significant influence on the occurrence of hepatotoxicity due to L-asparaginase chemotherapy including age and hypoalbuminemia (p=0.045, p=0.028). Conclusion: Age and hypoalbuminemia were the risk factors that might affect the incidents of hepatotoxicity. Clinical monitoring before and after treatment needs to be done to prevent poor outcomes.
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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 | 0.001 |
| 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".