Glycemic Control in a Clinic-Based Sample of Diabetics in M’Bour Senegal
Bibliographic record
Abstract
BACKGROUND: Sub-Saharan Africa (SSA) including Senegal is faced with a significant and increasing burden of type 2 diabetes. However, little information is available about diabetes management among Senegalese diabetics. PURPOSE: The current study aims to describe the level of glycemic control among a convenience sample of diabetics who receive care at the M'Bour Hospital in M'Bour, Senegal. METHODS: A total of 106 type 2 diabetic patients were recruited at the hospital complex of M'Bour, Senegal. Linear regression was employed to assess the relationship between clinical and sociodemographic factors and Hba1c. RESULTS: Only 24.8% of the sample had glycemic control, according to an Hba1c test. Participants who were diagnosed earlier were less likely to have diabetes control (mean = 7.8 years) compared with those who were diagnosed more recently (mean = 6.5 years); p< .05. CONCLUSIONS: We found that glycemic control in our sample was suboptimal. Length of time with diabetes was one of the key factors related to glycemic control. Length of time with diabetes is negatively associated with glycemic control. Early diagnosis and early glycemic control are essential to long-term glycemic control screening, and early detection for diabetes is uncommon given the general lack of health insurance and most people paying out of pocket for medical care. In the absence of universal health insurance, public health programs that provide blood sugar screenings for high-risk individuals would provide preliminary indication of abnormal glucose; however, subsequent diagnostic testing and follow-up may still be cost prohibitive.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".