Survey of Nongovernmental Organizations Providing Pediatric Cardiovascular Care in Low- and Middle-Income Countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 programs 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 programs as well as reduction in mortality and patient waiting lists.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 it