Building the ecosystem for pediatric neuro‐oncology care in Pakistan: Results of a 7‐year long twinning program between Canada and Pakistan
Bibliographic record
Abstract
BACKGROUND: Low- and middle-income countries sustain the majority of pediatric cancer burden, with significantly poorer survival rates compared to high-income countries. Collaboration between institutions in low- and middle-income countries and high-income countries is one of the ways to improve cancer outcomes. METHODS: Patient characteristics and effects of a pediatric neuro-oncology twinning program between the Hospital for Sick Children in Toronto, Canada and several hospitals in Karachi, Pakistan over 7 years are described in this article. RESULTS: A total of 460 patients were included in the study. The most common primary central nervous system tumors were low-grade gliomas (26.7%), followed by medulloblastomas (18%), high-grade gliomas (15%), ependymomas (11%), and craniopharyngiomas (11.7%). Changes to the proposed management plans were made in consultation with expert physicians from the Hospital for Sick Children in Toronto, Canada. On average, 24% of the discussed cases required a change in the original management plan over the course of the twinning program. However, a decreasing trend in change in management plans was observed, from 36% during the first 3.5 years to 16% in the last 3 years. This program also led to the launch of a national pediatric neuro-oncology telemedicine program in Pakistan. CONCLUSIONS: Multidisciplinary and collaborative efforts by experts from across the world have aided in the correct diagnosis and treatment of children with brain tumors and helped establish local treatment protocols. This experience may be a model for other low- and middle-income countries that are planning on creating similar programs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".