Screening for diabetes in Indigenous populations using glycated haemoglobin: sensitivity, specificity, post-test likelihood and risk of disease
Bibliographic record
Abstract
\n \t\t\tAIMS: Screening for diabetes using glycated haemoglobin (HbA1c) offers potential advantages over fasting glucose or oral glucose tolerance testing. Current recommendations advise against the use of HbA1c for screening but test properties may vary systematically across populations, according to the diabetes prevalence and risk. We aimed to: (i) characterize the properties of test cut-offs of HbA1c for diagnosis of diabetes relative to a diagnosis based on a fasting plasma glucose concentration of 7.0 mmol/l for high-risk Indigenous populations; and (ii) examine test properties across a range of diabetes prevalence from 5 to 30%. METHODS: Data were collected from Aboriginal and Torres Strait Islander communities in Australia and a Canadian First Nations community (diabetes prevalence 12-22%) in the course of diabetes diagnostic and risk factor screening programmes (n = 431). Screening test properties were analyzed for the range of HbA1c observed (3-12.9%). RESULTS: In separate and pooled analyses, a HbA1c cut point of 7.0% proved the optimal limit for classifying diabetes, with summary analysis results of sensitivity = 73 (56-86)%, specificity = 98 (96-99)%, overall agreement (Youden's index) = 0.71, and positive predictive value (for an overall prevalence of 18%) = 88%. For diabetes prevalence from 5 to 30% the post-test likelihood of having diabetes given HbA1c = 7.0% (positive predictive value) ranged from 62.7 to 93.2%; for HbA1c < 7.0%, the post-test likelihood of having diabetes ranged from 4.5 to 27.7%. CONCLUSIONS: The results converge with research on the likelihood of diabetes complications in supporting a HbA1c cut-off of 7.0% in screening for diabetes in epidemiological research. Glycated haemoglobin has potential utility in screening for diabetes in high-risk populations.\n
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 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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 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".