Growing Burden of Diabetes in Sub- Saharan Africa: Contribution of Pesticides ?
Bibliographic record
Abstract
The diabetes burden is growing in Sub-Saharan Africa (SSA). The low overall access to health care has been documented to contribute to the high diabetes-related mortality. Due to economic, demographic, epidemiological and nutrition transitions in SSA, the growing prevalence of diabetes appears to be related to obesogenic lifestyles and the intergenerational impact of malnutrition in women of childbearing age. Both overnutrition and undernutrition have been associated with the development of diabetes and other chronic diseases. Africans are also suspected of being genetically predisposed to diabetes. According to existing data in developed countries, exposure to pesticides, particularly organochlorines and metabolites, is associated with a higher risk of developing type 2 diabetes and its comorbidities. In African countries, pesticide exposure levels often appear much higher than in developed countries. Furthermore, undernutrition, which is still highly prevalent in SSA, could increase susceptibility to the adverse effects of organic pollutants. Therefore, the growing and inadequate use of pesticides may well represent an additional risk factor for diabetes in SSA. Additionally, high exposure to pesticides in African infants in utero and during the perinatal period may increase the intergenerational risk of developing diabetes in SSA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".