Gaps and barriers in the control of blood glucose in people with type 2 diabetes
Bibliographic record
Abstract
BACKGROUND: Glycaemic control is suboptimal in a large proportion of people with type 2 diabetes who are consequently at an increased and avoidable risk of potentially severe complications. We sought to explore attitudes and practices among healthcare professionals that may contribute to suboptimal glycaemic control through a review of recent relevant publications in the scientific literature. METHODS: An electronic search of the PubMed database was performed to identify relevant publications from January 2011 to July 2015. The electronic search was complemented by a manual search of abstracts from key diabetes conferences in 2014/2015 available online. RESULTS: Recently published data indicate that glycaemic control is suboptimal in a substantial proportion (typically 40%-60%) of people with diabetes. This is the case across geographic regions and in both low- and higher-income countries. Therapeutic inertia appears to be an important contributor to poor glycaemic control in up to half of people with type 2 diabetes. In particular, prescribers are often willing to tolerate extended periods of 'mild' hyperglycaemia as well as having low expectations for their patients. There are often delays of 3 years or longer in initiating or intensifying glucose-lowering therapy when needed. CONCLUSION: Many people with type 2 diabetes are failed by current management, with approximately half not achieving or maintaining appropriate target blood glucose levels, leaving these patients at increased and avoidable risk of serious complications. Review criteria: The methodology of this review article is detailed in the 'Methods' section.
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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".