Bibliographic record
Abstract
Sickle cell disease has been largely an invisible global health issue, especially in regions of high incidence mainly due to lack of awareness among both the local health policy makers and the public. Public health interventions, such as screening of newborns, provision of prophylaxis against bacterial infections, and immunizations against pneumococcal infections can have the greatest impact. Family education on assessment of spleen size and subsequent detection of splenic sequestration and promptness to seek medical attention for a febrile child is also important in the control of the morbidity and mortality of children with SCD living in resource-poor countries. In addition to these affordable interventions, hydroxyurea therapy is necessary to decrease both the acute and chronic complications of sickle cell anemia. Sickle cell disease has been recognized to have global health significance by key institutions including the World Health Organization in 2006 and the United Nation is 2008. In 2010, the WHO released national health care management goals and set targets to be achieved by the countries in sub-Saharan Africa for the control and management of SCD. These are yet to be translated into action. To do, this would require active and sustainable public-private partnerships for sustainable program development in these regions. Effective interventions should be integrated into existing health systems, the best examples linking primary healthcare facilities to specialized sickle cell disease centers in regional and tertiary healthcare institutions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".