Ethiopian paediatric oncology registry progress report: documentation practice improvements at tertiary care centre in Addis Ababa, Ethiopia
Bibliographic record
Abstract
Limited data are available regarding cancer in low and middle-income countries (LMICs), distorting the true burden of paediatric cancer.1 A sobering statistic based on available data shows that more than 80% of children diagnosed with cancer in high-income countries survive, while fewer than 25% of children in LMICs survive.2 While access to paediatric oncological care in Ethiopia is improving, the establishment of a national paediatric cancer registry remains an unmet need. Building on our previous work, we sought to standardise patient treatment documentation within the paediatric haematology and oncology department at Tikur Anbessa Specialized Hospital (TASH) in Addis Ababa, Ethiopia, to begin formal paediatric cancer registration at TASH.3 We interviewed medical record users and observed that there was a lack of consistency in treatment documentation as well as variability in the collection of data relating to cancer diagnoses. We attempted to address these gaps in documentation through the creation of two separate sets of data …
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".