Forecasting Asparaginase Need and Cost for Childhood Cancer Using ACCESS FORxECAST
Bibliographic record
Abstract
PURPOSE: Asparaginase (ASN) is a critical component of pediatric ALL protocols. Until recently, ASN was available in three formulations: native Escherichia coli, PEGylated E. coli (PEG), and Erwinase, with native E. coli typically more accessible in low- and middle-income countries (LMICs). Short shelf lives, intermittent availability, and concern for substandard formulations in LMICs have created a need for proactive ASN demand estimates. METHODS: We adapted FORxECAST, a pediatric cancer drug forecasting model, to focus on ASN for pediatric ALL. The model is adaptable to user data and defaults to best available public data where local data are unavailable. We forecast ASN quantity and cost in three case study countries for four scenarios using two regimens-base regimen (BR) and intensified regimen (IR)-outlining how quantity and costs vary on the basis of ASN formulation, dose, and second-line availability. RESULTS: Native E. coli is cheaper than PEG for first-line treatment across all scenarios. Regimen intensification from BR to IR requires a substantially higher cost when PEG is used relative to native E. coli. The cost of treating ASN hypersensitivity with Erwinase for BR in Burundi, Ghana, and Turkmenistan is $19,660 in US dollars (USD), $24,800 USD, and $15,246 USD, respectively. CONCLUSION: Treatment intensification requires a cost increase that should be accessible for most LMICs, but PEG utilization is substantially more costly, suggesting that prioritizing investment in intensifying treatment using native E. coli is the least costly approach to maximize treatment availability. Cost savings associated with native E. coli utilization may liberate funds for Erwinase procurement for patients with ASN hypersensitivity. Future analyses needed include an evaluation of costs associated with preventing abandonment due to compliance complexity with native E. coli given increased administration frequency compared with PEG.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".