[Diabetes in Ivory Coast: special epidemiological features].
Bibliographic record
Abstract
Within less than a quarter century diabetes has become a health problem in developing countries. In Africa this metabolic disorder is found in a wide variety of sometimes atypical forms. The purpose of this study was to highlight the special epidemiological features of medically diagnosed diabetes in Ivory Coast. Data from the files of 10320 African patients who presented at a major national outpatient care centre between January 1, 1991 and December 31, 2000 were compiled and analyzed. Findings showed that morbidity gradually increased from 30 to 49 years then stabilized from 50 to 69 years with a higher rate in males between 30 and 49 years. One of the five national ethnic groups appeared to be most affected and two appeared to be relatively unaffected. On the basis of several criteria, 5968 patients were classified as type 1 in 11.8% of cases, type 2 without excess body weight in 48.7% and type 2 with excess body weight in 39.5%. The second of these identified groups was characterized by intermediate-discovered glycaemia and older age at diagnosis. Epidemiological features included age of occurrence and higher morbidity in young male patients, probable higher premature mortality, likely links with socio-cultural environmental factors and existence of two type 2 subgroups. This profile underlines the challenges of screening, management and prevention of diabetes in Ivory Coast.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".