Breaking down the barriers to good glycaemic control in type 2 diabetes: a debate on the role of nurses
Bibliographic record
Abstract
The number of people with diabetes worldwide is projected to reach 380 million by 2025, with 90% of these cases attributed to type 2 diabetes. Diabetes can cause a range of long-term complications, including heart disease, stroke and blindness. Studies have shown that poor glycaemic control can increase the risk of developing these complications and guidelines have been developed that provide recommendations on how best to manage diabetes and encourage good glycaemic control. However, a number of barriers to achieving good glycaemic control remain and in many parts of the world treatment is suboptimal. It is generally agreed that glycosylated haemoglobin (HbA1c) testing represents the best way to monitor blood glucose levels. Yet many doctors lack the time and resources required to implement recommended guidelines on HbA1c monitoring. Consequently, patients have a lack of understanding of HbA1c testing and do not achieve target levels. Nurses have an important role to play in treating diabetes. Evidence demonstrates that, through patient support and education, nurses have a notable, positive impact on the proportion of patients achieving HbA1c targets. Given the epidemic proportions of diabetes worldwide, the importance of nurses in diabetes management is likely to increase further in the coming years.
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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 | 0.072 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.013 | 0.026 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.028 | 0.040 |
| Insufficient payload (model declined to judge) | 0.008 | 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".