Management of type 2 diabetes using non‐insulin glucose‐lowering therapies: a critical appraisal of clinical practice guidelines with the AGREE II instrument
Bibliographic record
Abstract
AIM: Type 2 diabetes is a major global epidemic affecting over 400 million people worldwide. The objective of this systematic review was to provide an overview of recommendations from clinical practice guidelines (guidelines) addressing non-insulin based pharmacological management of among non-pregnant adults in an outpatient setting, and critically appraise their methodological development. METHODS: We systematically searched MEDLINE and Embase databases, for relevant guidelines using the Ovid interface. We scanned the bibliographies of all eligible guidelines for additional relevant citations. Teams of two reviewers, independently and in duplicate, screened titles and abstracts and potentially eligible full text reports to determine eligibility and appraised the reporting quality of guidelines using the Advancing Guideline Development, Reporting and Evaluation in Health Care instrument II (AGREE II) instrument. RESULTS: Our search yielded 11264 unique citations, of which 124 were retrieved for full-text review; 17 guidelines proved eligible. The highest scoring AGREE domain was 'clarity of presentation' (66%; range 7-92%), followed by 'scope and purpose' (58%; range 25-92%), 'editorial independence' (55%; range 0-91%), 'stakeholder involvement' (45%; range 11-90%) and 'rigour of development' (43%; range 4-92%). The poorest domain was 'applicability' (37%; range 6-84%). The guidelines authored by the World Health Organization group achieved the highest AGREE overall score. CONCLUSIONS: Most of the guidelines provided recommendations with a local jurisdictional focus and showed significant variation in the quality. Nevertheless, only a small number of those scored well overall.
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 | 0.189 | 0.467 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.027 | 0.025 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".