Training pediatric hematologist/oncologists for capacity building in Ethiopia
Bibliographic record
Abstract
PURPOSE: A considerable barrier to global pediatric oncology efforts has been the scarcity and even absence of trained professionals in many low- and middle-income countries, where the majority of children with cancer reside. In 2013, no dedicated pediatric hematology-oncology (PHO) programs existed in Ethiopia despite the estimated annual incidence of 6000-12000 cases. The Aslan Project initiative was established to fill this gap in order to improve pediatric cancer care in Ethiopia. A major objective was to increase subspecialty PHO-trained physicians who were committed to practicing locally and empowered to lead programmatic development. METHODS: We designed and implemented a PHO training curriculum to provide a robust educational and clinical experience within the existing resource-constrained environment in Ethiopia. Education relied on visiting PHO faculty, a training attachment abroad, and extraordinary initiative from trainees. RESULTS: Four physicians have completed comprehensive PHO subspecialty training based primarily in Ethiopia, and all have remained local. Former fellows are now leading two PHO centers in Ethiopia with a combined capacity of 64 inpatient beds and over 800 new diagnoses per year; an additional former fellow is developing a pediatric cancer program in Nairobi, Kenya. Two fellows currently are in training. Program leadership, teaching, and advocacy are being transitioned to these physicians. CONCLUSIONS: Despite myriad challenges, a subspecialty PHO training program was successfully implemented in a low-income country. PHO training in Ethiopia is approaching sustainability through human resource development, and is accelerating the growth of dedicated PHO services where none existed 7 years ago.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".