Access to Care and Therapy for Kawasaki Disease in the Arab Countries: A Kawasaki Disease Arab Initiative (Kawarabi) Multicenter Survey:
Bibliographic record
Abstract
Abstract Kawasaki Disease (KD) is still the most common acquired heart disease in children below the age of five years; it has been well described in the developed world; however, data from the Arab world are limited to case reports or single-center case series. In an effort of optimizing KD research in the Arab world, a group of physicians and researchers established the KD Arab Initiative (Kawarabi) in 2021, and published the first survey, which showed disparities in the availability of intravenous immunoglobulin (IVIG); this had prompted Kawarabi to assess the access to care and therapy of KD patients in Arab countries. A 32 structured questions survey was conducted in thirteen Arab countries and addressed KD patients' access to healthcare in urban and rural settings. The survey results showed that access to care was uniform across large, mid-size cities and rural areas in 7/13 (54%) countries, while in 6/13 (46%) countries, it was in favor of large and mid-size cities over rural areas. The quality of medical services received by children with KD in large cities was rated as excellent in 6/13 or good in 7/13 countries compared to fair in 4/13 or poor in 4/13 countries in rural areas. Availability of IVIG was limited (23%) in mid-size cities and almost impossible (23%) in rural areas. The KD patients in mid-size cities and rural areas have limited access to standard healthcare in the Arab world. This survey laid the foundation for future Kawarabi endeavors to improve the care of children with KD.
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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 | 0.002 |
| 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) | 0.001 | 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".