Forecasting global essential childhood cancer drug need and cost: An innovative model-based approach.
Bibliographic record
Abstract
e19078 Background: Childhood cancer outcomes in low-middle income countries (LMICs) have not kept pace with advances in care and survival in high income countries (HICs). A contributing factor to this survival gap is unreliable access to essential cancer drugs. Lack of data on the aggregate need and cost of essential cancer drugs has hampered rational planning and acquisition in many LMICs. Methods: We created a pediatric-specific tool (FORxECAST) that estimates drug quantity and cost for 18 pediatric cancers, customizable to region, regimen, cancer stage distribution, and drug price. We used adapted treatment regimens developed by the International Society of Pediatric Oncology (SIOP), supplemented with input from disease experts, to model treatment approaches reflective of health-system capabilities. FORxECAST incorporates incidence data generated through microsimulation estimates of both diagnosed and undiagnosed (total) cases. Results: We created a pediatric-specific tool (FORxECAST) that estimates drug quantity and cost for 18 pediatric cancers, customizable to region, regimen, cancer stage distribution, and drug price. We used adapted treatment regimens developed by the International Society of Pediatric Oncology (SIOP), supplemented with input from disease experts, to model treatment approaches reflective of health-system capabilities. FORxECAST incorporates incidence data generated through microsimulation estimates of both diagnosed and undiagnosed (total) cases. Conclusions: Our results enable evidence-based forecasting of childhood cancer drug need and cost to inform health system planning in a wide range of countries. The model is adaptable to setting, diagnosis, and treatment approach, allowing decision-makers to generate results specific to their context and needs. Global estimates of essential childhood cancer drug need and cost demonstrate the comparatively small amount of aggregate resources required to treat all cases worldwide, and can help advance innovative procurement strategies with regional and international scale that drive global improvements in childhood cancer drug access.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".