Capacity building for pediatric neuro-oncology in Pakistan- a project by my child matters program of Foundation S
Bibliographic record
Abstract
Introduction: Initiated in June 2019, this collaborative effort involved 15 public and private sector hospitals in Pakistan. The primary objective was to enhance the capacity for pediatric neuro-oncology (PNO) care, supported by a My Child Matters/Foundation S grant. Methods: We aimed to establish and operate Multidisciplinary Tumor Boards (MTBs) on a national scale, covering 76% of the population (185.7 million people). In response to the COVID-19 pandemic, MTBs transitioned to videoconferencing. Fifteen hospitals with essential infrastructure participated, holding monthly sessions addressing diagnostic and treatment challenges. Patient cases were anonymized for confidentiality. Educational initiatives, originally planned as in-person events, shifted to a virtual format, enabling continued implementation and collaboration despite pandemic constraints. Results: A total of 124 meetings were conducted, addressing 545 cases. To augment knowledge, awareness, and expertise, over 40 longitudinal lectures were organized for healthcare professionals engaged in PNO care. Additionally, two symposia with international collaborators and keynote speakers were also held to raise national awareness. The project achieved significant milestones, including the development of standardized national treatment protocols for low-grade glioma, medulloblastoma, and high-grade glioma. Further protocols are currently under development. Notably, Pakistan's first pediatric neuro-oncology fellowship program was launched, producing two graduates and increasing the number of trained pediatric neuro-oncologists in the country to three. Discussion: The initiative exemplifies the potential for capacity building in PNO within low-middle income countries. Success is attributed to intra-national twinning programs, emphasizing collaborative efforts. Efforts are underway to establish a national case registry for PNO, ensuring a comprehensive and organized approach to monitoring and managing cases. This collaborative initiative, supported by the My Child Matters/Foundation S grant, showcases the success of capacity building in pediatric neuro-oncology in low-middle income countries. The establishment of treatment protocols, fellowship programs, and regional tumor boards highlights the potential for sustainable improvements in PNO care.
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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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".