Clinical Inertia in the Management of Type 2 Diabetes Mellitus: A Systematic Review
Bibliographic record
Abstract
This review seeks to establish, through the recent available literature, the prevalence of therapeutic intensification delay and its sequences in poorly controlled Type 2 Diabetes Mellitus (T2DM) patients. The strategy identified studies exploring the clinical inertia and its associated factors in the treatment of patients with T2DM. A total of 25 studies meeting the pre-established quality criteria were included in this review. These studies were conducted between 2004 and 2021 and represented 575,067 patients diagnosed with T2DM. Trusted electronic bibliographic databases, including Medline, Embase, and the Cochrane Central Register of Controlled Trials, were used to collect studies by utilizing a comprehensive set of search terms to identify Medical Subject Headings (MeSH) terms. Most o the studies included in this review showed clinical inertia rates over 50% of T2DM patients. In the USA, clinical inertia ranged from 35.4% to 85.8%. In the UK, clinical inertia ranged from 22.1% to 69.1%. In Spain, clinical inertia ranged from 18.1% to 60%. In Canada, Brazil, and Thailand, clinical inertia was reported as 65.8%, 68%, and 68.4%, respectively. The highest clinical inertia was reported in the USA (85.8%). A significant number of patients with T2DM suffered from poor glycemic control for quite a long time before treatment intensification with oral antidiabetic drugs (OADs) or insulin. Barriers to treatment intensification exist at the provider, patient, and system levels. There are deficiencies pointed out by this review at specialized centers in terms of clinical inertia in the management of T2DM including in developed countries. This review shows that the earlier intensification in the T2DM treatment is appropriate to address issues around therapeutic inertia.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".