Cost effectiveness of support with out-of-pockets costs to prevent treatment abandonment in Malawi and sub-Saharan Africa; lessons learnt and the way forward – a report from CANCaRe Africa
Bibliographic record
Abstract
Cost effectiveness of support with out-of-pockets costs to prevent treatment abandonment in Malawi and sub-Saharan Africa; lessons learnt and the way forward – a report from CANCaRe Africa Junious Sichali1, Avi Denburg2, Harriet Khofi3, Cecilia Mdoka1, Deborah Nyirenda4, Yamikani Chimalizeni3, George Chagaluka3, Elizabeth Molyneux3, Marc Y. R. Henrion4,5, Sumit Gupta2, Trijn Israels11 Collaborative African Network for Childhood Cancer Care and Research (CANCaRe Africa), 2Division of Haematology/Oncology, Hospital for Sick Children, Toronto, Canada,3 Kamuzu University of Health Sciences (KUHeS), Blantyre, Malawi, 4 Malawi Liverpool Wellcome Research Programme, Blantyre, Malawi, 5Liverpool School of Tropical Medicine, Liverpool, UKCorresponding author:Dr Trijn Israels, CANCaRe Africa, Department of Paediatrics, Kamuzu University of Health Sciences (KuHES), Blantyre, Malawi. Email: cancareafrica@gmail.comWord count: 1189 wordsNumber of Tables: 0Number of Figures: 0Short running title: Cost-effectiveness of treatment abandonment preventionKey words: childhood cancer, treatment abandonment, cost-effectiveness,LMIC Low- and middle-income countryGICC Global Initiative for Childhood CancerCANCaRe Africa Collaborative African Network for Childhood Cancer Care and ResearchALL Acute lymphoblastic leukaemiaGDP Gross Domestic ProductDALY Disability Adjusted Life Year
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".