A global overview of SCD: populations, policy limitations, and urgent need for comprehensive SCD clinical care, a systematic review
Bibliographic record
Abstract
Abstract SCD globally affects over 30 million people and is most prevalent in sub-Saharan Africa, India, the Arabian Peninsula, the Caribbean, and North/South America. Globally, people living with SCD disproportionately suffer premature deaths, hospitalizations due to acute complications, and significant multi-organ complications. Despite vivid similarities with cystic fibrosis (CF), clinical care and research is disproportionately minimal for SCD. Both CF and SCD are inherited, life-limiting, multi-system diseases; however, one mainly affects White people and the other Black people. We aim to describe socio-demographics of SCD populations globally and highlight policy limitations and urgently needed changes to achieve equitable and just SCD care and research. An electronic database search of Ovid MEDLINE (“sickle cell disease and marginalized people” and “policy in sickle cell disease”) was conducted for the period 1947 to May 2022. Additional information was obtained through Google Scholar, gray literature, and back references of relevant articles. Study selection and quality assessment was conducted independently in duplicate. Data were extracted and analyzed from 137 full articles, reports, and gray literature. We propose 5 main actionable items: (1) establish and strengthen national and international screening programs; (2) implement prevention and education programs; (3) enhance collaboration between stakeholders; (4) increase funding for SCD-related research; and (5) promote new models for multidisciplinary and transition care. Globally, social, economic, geographical, and political factors affect access to comprehensive SCD management. Urgent policy changes are needed for equitable, inclusive, and just SCD care with lifespan approach, and research.
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.
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 | 0.015 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".