Time to onset of type 2 diabetes mellitus in Ghana
Bibliographic record
Abstract
BACKGROUND: Type 2 diabetes affects an increasing number of Ghanaians. The timing of the onset of diabetes is linked to several other co-morbid conditions, yet no study has examined the timing of the onset of type 2 diabetes in Ghana. METHODS: To fill this gap in the literature, this study applied logit models to data extracted from the medical records at the Diabetes Clinic of the Komfo Anokye Teaching Hospital in Kumasi, Ghana. Gender-specific models were also estimated. RESULTS: The results show that obesity was a significant predictor of the timing of the first onset of diabetes among both males and females. Women with high school education compared with no formal education, and female employees compared with the unemployed were more likely to experience an early onset of type 2 diabetes. CONCLUSION: Policymakers must educate Ghanaians about behaviors that will reduce their risk of obesity and diabetes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.001 |
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 teacher head, 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".