Bridging the Distance in the Caribbean: Telemedicine as a means to build capacity for care in paediatric cancer and blood disorders
Bibliographic record
Abstract
Over the past 50 years, survival for children in high-income countries has increased from 30% to over 80%, compared to 10-30% in low and middle income countries (LMIC). Given this gap in survival, established paediatric cancer treatment centres, such as The Hospital for Sick Children (SickKids) are well positioned to share clinical expertise. Through the SickKids Centre for Global Child Health, the SickKids-Caribbean Initiative (SCI) was launched in March 2013 to improve the outcomes and quality of life for children with cancer and blood disorders in the Caribbean. The six participating Caribbean countries are among those defined by the United Nations as Small Island Developing States, due to their small size, remote location and limited accessibility. Telemedicine presents an opportunity to increase their accessibility to health care services and has been used by SCI to facilitate two series of interprofessional rounds. Case Consultation Review Rounds are a forum for learning about diagnostic work-up, management challenges and treatment recommendations for these diseases. To date, 54 cases have been reviewed by SickKids staff, of which 35 have been presented in monthly rounds. Patient Care Education Rounds provide nurses and other staff with the knowledge base needed to safely care for children and adolescents receiving treatment. Five of these rounds have taken place to date, with over 200 attendees. Utilized by SCI for both clinical and non-clinical meetings, telemedicine has enhanced opportunities for collaboration within the Caribbean region. By building capacity and nurturing expert knowledge through education, SCI hopes to contribute to closing the gap in childhood survival between high and low-resource settings.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".