The value of reporting on end-of-treatment outcome of patients in low-income settings
Bibliographic record
Abstract
The value of reporting on end-of-treatment outcome of patients in low-income settingsTrijn Israels MD PhD1,2, Ramandeep Singh Arora DCH, MD3, Lillian Sung MD PhD41 Collaborative African Network for Childhood Cancer Care and Research (CANCaRe Africa), 2 Kamuzu University of Health Sciences, Blantyre, Malawi, 3 Max Super Speciality Hospital, New Delhi, India, 4 Sick Children’s Hospital, Toronto, CanadaCorresponding author:Dr Trijn Israels, CANCaRe Africa, Department of Paediatrics, Kamuzu University of Health Sciences (KuHES), Blantyre, Malawi. Email: cancareafrica@gmail.comWord count: 1256 wordsNumber of Tables: 0Number of Figures: 0Short running title: End-of-treatment outcome reportingKey words: childhood cancer, survival, LIC, indicatorsLIC Low-income countryHIC High-income countryGICC Global Initiative for Childhood CancerEFS Event-free survivalOS Overall survivalTRM Treatment related mortalityDRM Disease related mortalityCANCaRe Africa Collaborative African Network for Childhood Cancer Care and Research
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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 | 0.145 | 0.470 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".