Forecasting essential childhood cancer drug need: An innovative model‐based approach
Bibliographic record
Abstract
Background Childhood cancer outcomes in low‐income and middle‐income countries have not kept pace with advances in care and survival in high‐income countries. A contributing factor to this survival gap is unreliable access to essential drugs. Methods The authors created a tool (FORxECAST) capable of predicting drug quantity and cost for 18 pediatric cancers. FORxECAST enables users to estimate the quantity and cost of each drug based on local incidence, stage breakdown, treatment regimen, and price. Two country‐specific examples are used to illustrate the capabilities of FORxECAST to predict drug quantities. Results On the basis of domestic public‐sector price data, the projected annual cost of drugs to treat childhood cancer cases is 0.8 million US dollars in Kenya and 3.0 million US dollars in China, with average median price ratios of 0.9 and 0.1, respectively, compared with costs sourced from the Management Sciences for Health (MSH) International Medical Products Price Guide. According to the cumulative chemotherapy cost, the most expensive disease to treat is acute lymphoblastic lymphoma in Kenya, but a higher relative unit cost of methotrexate makes osteosarcoma the most expensive diagnosis to treat in China. Conclusions FORxECAST enables needs‐based estimates of childhood cancer drug volumes to inform health system planning in a wide range of contexts. It is broadly adaptable, allowing decision makers to generate results specific to their needs. The resultant estimates of drug need can help equip policymakers and health governance institutions with evidence‐informed data to advance innovative procurement strategies 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".