An innovative virtual fellowship leveraging global and regional mentorship to foster pediatric neuro-oncologists in low/middle-income countries
Bibliographic record
Abstract
Abstract: BackgroundMost children with central nervous system (CNS) tumors reside in low- and middle-income countries (LMICs), with limited availability of trained pediatric neuro-oncologists. Methods: Using a series of structured interviews of physicians who had served as global mentors or mentees in pediatric oncology, we identified mentorship, leadership, and clinical training as key components necessary to virtually train pediatric oncologists in LMICs to become leading pediatric neuro-oncologists while they remain in their home countries. Thus, the St Jude Global Virtual Pediatric Neuro-oncology Fellowship (VPNOF) was designed to incorporate mentorship with global and loco-regional mentors to aid in each fellow's career and institutional goal setting and clinical training involving virtual tumor boards and didactics and ad-hoc case discussions, enabling fellows to manage patients at their home institution. Fellows traveled to their mentors' institutions twice for four-week clinical rotations. Results: In 2022 and 2023, eleven fellows were selected, representing 10 LMICs. The 2-year fellowship led to the establishment of multi-disciplinary approaches, increased patient volume, increased use of evidence-based practices, 33 abstract presentations, and publication of four journal articles. Conclusions: The VPNOF is an innovative approach leveraging global mentorship to train pediatric oncologists in resource-limited settings to become pediatric neuro-oncologists, which has led to the successful implementation of new practice paradigms to improve the quality of care for children with CNS tumors in LMICs.
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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 | 0.003 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".