Original Article Survey of Nongovernmental Organizations Providing Pediatric Cardiovascular Care in Low- and Middle-Income Countries
Bibliographic record
Abstract
Background: Nearly 90 % of the children with heart disease in low- and middle-income countries (LMICs) cannot access cardiovascular (CV) services. Limitations include inadequate financial, human, and infrastructure resources. Nongovernmental organizations (NGOs) have played crucial roles in providing clinical services and infrastructure supports to LMICs CV programs; however, these outreach efforts are dispersed, inadequate, and lack coordination. Methods: A survey was sent to members of the World Society for Pediatric and Congenital Heart Society and PediHeart. Results: A clearinghouse was created to provide information on NGO structures, geographic reach, and scope of services. The survey identified 80 NGOs supporting CV pro-grams in 92 LMICs. The largest outreach efforts were in South and Central America (42%), followed by Africa (18%), Europe (17%), Asia (17%), and Asia-Western Pacific (6%). Most NGOs (51%) supported two to five outreach missions per year. The majority (87%) of NGOs provided education, diagnostics, and surgical or catheter-based interventions. Working jointly with LMIC partners, 59 % of the NGOs performed operations in children and infants; 41 % performed nonbypass neonatal operations. Approximately a quarter (26%) reported that partner sites do not perform interventions in between missions. Conclusions: Disparity and inadequacy in pediatric CV services remain an important problem for LMICs. A global consensus and coordinated efforts are needed to guide strategies on the development of regional centers of excellence, a global outcome database, and a CV program registry. Future efforts should be held accountable for impacts such as growth in the number of independent LMIC
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".