Diabetes and intermediate hyperglycaemia in Kisantu, DR Congo: a cross-sectional prevalence study
Bibliographic record
Abstract
OBJECTIVES: To study the prevalence and risk markers of diabetes mellitus and intermediate hyperglycaemia (IH) in Kisantu, a semirural town in Bas-Congo province, The Democratic Republic of Congo. DESIGN: A cross-sectional population-based survey. SETTINGS: A modified WHO STEPwise strategy was used. Capillary glycaemia was measured for fasting plasma glucose and 2-h-postload glucose. Both WHO/IDF (International Diabetes Federation) 2006 and American Diabetes Association (ADA) 2003 diagnostic criteria for diabetes and IH were used. PARTICIPANTS: 1898 subjects aged ≥ 20 years. RESULTS: Response rate was 93.7%. Complete data were available for 1759 subjects (86.9%). Crude and standardised (for Doll and UN population) prevalence of diabetes were 4.8% and 4.0-4.2%. Crude IH prevalence was 5.8% (WHO/IDF) and 14.2% (ADA). Independent risk markers for diabetes (p<0.01) were male (OR 2.5), age 50-69 years (OR 2.6), family history (OR 3.5), waist (OR 4.1) and alcohol consumption (OR 0.36). In receiver operating characteristic (ROC) analysis, prediction of diabetes was slightly better by waist than body mass index (BMI). IH defined according to WHO/IDF was associated with BMI (OR 2.6, p<0.001). IH defined according to ADA was associated (p<0.05) with waist (OR 1.4), education level (OR 1.6), BMI (OR 2.4) and physical activity (OR 0.7). CONCLUSIONS: Current prevalence of diabetes in DR Congo exceeds IDF projections for 2030. The lower glucose threshold used by ADA almost triples impaired fasting glucose prevalence compared to WHO/IDF criteria. The high proportion of disorders of glycaemia made up by IH suggests the early stages of a diabetes epidemic.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".