Quality appraisal of gestational diabetes mellitus guidelines with AGREE II: a systematic review
Bibliographic record
Abstract
BACKGROUND: Several societies and associations have produced and disseminated clinical practice guidelines (CPGs) for gestational diabetes mellitus (GDM). However, the quality of such guidelines has not been appraised so far. This study aims to evaluate the quality of CPGs for GDM published in the last decade using the AGREE II instrument. METHODS: A systematic search of the National Institute for Health and Care Excellence, New Zealand Guidelines Group, Scottish Intercollegiate Guidelines Network, Medlive, American Diabetes Association, Canadian Diabetes Association, International Diabetes Federation, as well as PubMed, Web of Science, Embase, China National Knowledge Infrastructure, Wanfang Chinese Periodical Database, and VIP Chinese Periodical Database was conducted from inception to June 2018. The quality was assessed by four trained researchers independently, using the AGREE IIinstrument. RESULTS: A total of 13 guidelines, published from 2009 to 2018, were finally included. Among them, 11 guidelines were evidence-based guidelines, and 2 were expert consensus. Scores for each of the six AGREE II domains(Median ± IQR) were 94 ± 11, 89 ± 53, 58 ± 37, 100 ± 6, 79 ± 48, 100 ± 71 and 67% ± 42%, and guidelines based on expert consensus generally scored lower than evidence-based guidelines (Z = -2.201, p = 0.028). Overall score of 10 guidelines were 5 points and above, and four guidelines were 7 points. Among six domains, two domains: Scope and Purpose, and Clarity of Presentation, had high scores; however, the domains of Rigor of Development, Stakeholder Involvement and Editorial Independence received lower scores. CONCLUSIONS: In general, the methodological quality of GDM guidelines is high, and evidence-based guidelines are superior to expert consensus. However, the domains of Rigor of Development, Stakeholder Involvement and Editorial Independence still need improvement. A systematic approach in the development of these guidelines and updating timely is needed. In some regions, more attention for guideline adaptation is recommended.
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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 | 0.127 | 0.350 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.015 |
| Bibliometrics | 0.024 | 0.024 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| 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".