Asparaginase activity monitoring and management of asparaginase hypersensitivity reactions in Canada
Bibliographic record
Abstract
Introduction Pegaspargase can cause anti-asparaginase antibody formation, which can decrease its effectiveness without causing any clinically apparent reaction (silent inactivation). When a patient has silent inactivation, a switch to Erwinia anti-asparaginase is warranted, but there is currently a global shortage of Erwinia. The only way to identify silent inactivation is to measure an asparaginase level. However, routine asparaginase level monitoring is not currently standard of care at all Canadian centers. This study aims to identify variations in practice regarding asparaginase level monitoring and Erwinia use. Methods A 21-item survey was developed using OPINIO software and distributed to all Pediatric Hematology–Oncologists in Canada from February to October 2020. Results Respondents represented 15 hospitals across each region of Canada (response rate = 52%). Only 39.2% of respondents reported routinely measuring asparaginase levels, yet 53% of respondents have modified therapy from pegaspargase to Erwinia in up to half of their patients. The most common reason for not measuring asparaginase levels was not knowing how to use levels clinically (25.5%). There was variation in the timing of levels and their target. Conclusions We identified substantial variation in asparaginase activity monitoring practices across Canada. Therefore, future research should aim to develop a national practice guideline on asparaginase activity monitoring.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".