Results of a pre-implementation analysis of Ethiopia’s National Pediatric Cancer Registry
Bibliographic record
Abstract
In Ethiopia, cancer accounts for about 5.8% of total national mortality, with an estimated annual incidence of cancer of approximately 60,960 cases and an annual mortality of over 44,000 persons. This is likely an underestimation. Survival rates for pediatric malignancies are likewise suboptimal although exact figures are unknown since a national cancer registry is unavailable. The World Health Organization (WHO) provides recommendations for the creation of cancer registries to track such data. Here we describe our pharmacist-led, pre-implementation assessment of introducing an enhanced national pediatric cancer registry in Ethiopia. Our assessment project had three specific aims around which the methods were designed: 1) characterization of the current spreadsheet-based tool across participating sites, including which variables were being collected, how these variables compared to standards set by the WHO, and a description of how the data were entered and its completeness; 2) assessment of the perceptions of an enhanced registry from hospital staff; and 3) evaluation of workflow gaps regarding documentation. The hospital staff and leadership have generally positive perceptions of an enhanced pediatric cancer registry, which were further improved by our interactions. The workflow assessment revealed several gaps, which were addressed systematically using a three-phase implementation science approach. The assessment also demonstrated that the existing spreadsheet-based tool was missing WHO-recommended variables and had inconsistent completion due to the workflow gaps. A pediatric oncology summary sheet will be implemented in upcoming trips in patient charts to better summarize the patients' journey starting from diagnosis. This document will be used by the data clerks in an enhanced-spreadsheet to have a more complete data set.
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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 | 0.051 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".