Does age impact adverse events in adolescents and young adults treated with PEG-asparaginase treatment at a Canadian pediatric tertiary hospital? A retrospective review examining the role of age in the presentation of PEG-asparaginase side effects
Bibliographic record
Abstract
Background: Adolescents and young adults (AYA) with cancer have less favorable outcomes than other populations for lymphoid malignancies. Pediatric patients have a better survival rate for lymphoid malignancies and current evidence suggests that asparaginase plays a role in improved response to treatment. This study aimed to evaluate if AYA patients at a pediatric hospital were more likely to experience PEG-asparaginase (PEG-ASP) related adverse events than younger patients. Methods: A retrospective chart review from 2007-2017 was conducted in the pediatric population at the Children’s Hospital of Eastern Ontario (CHEO). Only patients having received PEG-ASP were included. Event incidence and risk related to age at diagnosis was assessed through parameter estimates and Wald Chi Square analysis. Results: In total, 75 adverse events were observed: 34/186 (18.3%) experienced allergic reactions, 8/186 (4.3%) pancreatitis, 31/186 (16.7%) thrombosis and 2/186 (1.1%) hemorrhage. 182 patients had complete information for inclusion in in our model. A correlation between age at diagnosis and higher risk of allergic reaction (p<0.001) and pancreatitis (p<0.035) was observed. Conclusion: Allergic reaction and pancreatitis upon administration of PEG-ASP have a higher risk of occurrence as age of diagnosis increases. This includes the AYA population and warrants precaution as PEG-ASP is included in older populations treatment regimens at pediatric centers.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".