Random capillary blood glucose in the diagnosis of diabetes: a cross-sectional study in Bangladesh
Bibliographic record
Abstract
OBJECTIVE: To assess the effectiveness of random capillary blood glucose as a diagnostic tool for type 2 diabetes and determine optimal cut-off values for adults in Bangladesh. DESIGN: Cross-sectional diagnostic accuracy study. SETTING: 16 diabetes centres were selected randomly from all eight administrative divisions of Bangladesh. PARTICIPANTS: A total of 3200 adults aged 18 years and older were recruited using systematic random sampling between May and September 2022. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was the diagnostic accuracy of random capillary blood glucose compared to fasting plasma glucose, 2-hour plasma glucose after a 75-gram glucose load and glycated haemoglobin. Secondary outcomes included sensitivity, specificity, area under the curve and agreement with the other diagnostic tests. RESULTS: Random capillary blood glucose showed a strong positive correlation and high concordance with fasting plasma glucose, 2-hour plasma glucose and glycated haemoglobin. A cut-off value of ≥8.7 mmol/L demonstrated improved diagnostic performance compared with the currently used cut-off of ≥11.1 mmol/L. This new threshold yielded higher sensitivity, specificity, area under the curve and agreement with other standard diagnostic tests. Notably, hyperglycaemic symptoms were not required for diagnosis. The number needed to screen to identify one case of type 2 diabetes using the ≥8.7 mmol/L cut-off was 2.74, lower than that for fasting plasma glucose (2.86) and random capillary blood glucose ≥11.1 mmol/L (4.68). CONCLUSIONS: Random capillary blood glucose may be an effective and affordable diagnostic tool for type 2 diabetes in resource-limited settings. The proposed cut-off of ≥8.7 mmol/L offers improved diagnostic accuracy and reflects the population's glucose distribution pattern.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".