Randomised controlled trial of near-patient testing for glycated haemoglobin in people with type 2 diabetes mellitus.
Bibliographic record
Abstract
BACKGROUND: Tight glycaemic control in people with type 2 diabetes can lead to a reduction in microvascular and possibly macrovascular complications. The use of near-patient (rapid) testing offers a potential method to improve glycaemic control. AIM: To assess the effect and costs of rapid testing for glycated haemoglobin (HbA1c) in people with type 2 diabetes. DESIGN OF STUDY: Pragmatic open randomised controlled trial. SETTING: Eight practices in Leicestershire, UK. METHOD: Patients were randomised to receive instant results for HbA1c or to routine care. The principal outcome measure was the proportion of patients with an HbA1c <7% at 12 months. We also assessed costs for the two groups. RESULTS: Of the 681 patients recruited to the study 638 (94%) were included in the analysis. The mean age at baseline was 65.7 years (SD = 10.8 years) with a median (interquartile range) duration of diabetes of 4(1-8) years. The proportion of patients with HbA1c < 7% did not differ significantly between the intervention and control groups (37 versus 38%, odds ratio 0.95 [95% confidence interval = 0.69 to 1.31]) at 12 months follow up. The total cost for diabetes-related care was 390 UK pounds per patient for the control group and 370 UK pounds for the intervention group. This difference was not statistically significant. CONCLUSION: Near-patient testing for HbA1c alone does not lead to outcome or cost benefits in managing people with type 2 diabetes in primary care. Further research is required into the use of rapid testing as part of an optimised patient management model including arrangements for patient review and testing.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".