LMIC-26. DEVELOPMENT OF THE ST. JUDE GLOBAL VIRTUAL PEDIATRIC NEURO-ONCOLOGY FELLOWSHIP (VPNOF): INCREASING PEDIATRIC NEURO-ONCOLOGY CAPACITY IN LOW- AND MIDDLE- INCOME COUNTRIES (LMIC)
Bibliographic record
Abstract
Abstract BACKGROUND Most children with central nervous system (CNS) tumors reside in LMICs, yet their complex management is limited by the availability of trained pediatric neuro-oncologists (PNO). The St. Jude Global VPNOF was designed to train pediatric oncologists (PO) in LMICs as CNS tumor experts while remaining in their home countries. METHODS The VPNOF structure was initially developed through semi-structured interviews of physicians who had served as global mentors or mentees in pediatric oncology. Employing a modified Delphi method, we identified the key components necessary to virtually train POs in LMICs to become PNOs. RESULTS A two-year fellowship program was designed with a curriculum tailored for POs in LMICs. Two main components were identified as critical to the objective of building PNO capacity: mentorship and clinical training. Mentorship involves a triad with one global and one loco-regional mentor per fellow, aiding in the fellow’s career and institutional goal setting to advance PNO care. Clinical training includes regularly scheduled virtual tumor boards and didactics, and ad-hoc case discussions with mentors while managing patients at their home institution. Additionally, fellows travel to their mentors’ institution twice for a four-week clinical rotation. In 2022, five fellows from Armenia, China, Indonesia, Mexico, and Pakistan were selected. In 2023, an additional six fellows were selected from China, Costa Rica, India, Romania, South Africa, and Sri Lanka. To date, 18 months of the two-year fellowship have resulted in the establishment of multi-disciplinary approaches, increased patient volume, increased evidence-based practices, and publication by the first cohort of 21 abstracts and two journal publications to date. CONCLUSIONS The VPNOF is an innovative approach to leveraging global mentorship to train PO in resource-limited settings to become PNOs. Virtual mentorship and training have led to implementation of new practices aimed at improving quality of care for children with CNS tumors in LMICs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".