Quality appraisal of clinical practice guidelines for diabetes mellitus published in China between 2007 and 2017 using the AGREE II instrument
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to systematically evaluate the quality of the clinical practice guidelines (CPGs) for diabetes mellitus published in China over the period of January 2007 to April 2017. METHODS: We searched the China National Knowledge Infrastructure, Chinese Biomedical Literature database, VIP database and WanFang databases and guideline websites for CPGs for diabetes mellitus published between January 2007 and April 2017 in China. Two reviewers independently screened the literature according to the inclusion and exclusion criteria and extracted data. We used the the Appraisal of Guidelines for Research and Evaluation II (AGREE II) tool (Canadian Institutes of Health Research, Ottawa, Canada) to evaluate the quality of the included guidelines, calculated the scores of each domain and evaluated the consistency among the assessors via use of the intragroup correlation coefficient. And then we compared the results with Chinese CPGs and international CPGs. We conducted a subgroup analysis based on different classification criteria and compared scores of each domain subgroup analyses. RESULTS: A total of 98 guidelines were identified. The correlation coefficient within the group was 0.93, suggesting that the consistency between the evaluators was good. The scores of the six domains of AGREE II were described in median (IQR) as follows: scope and purpose 53.7 (50.0-59.7), stakeholder involvement 31.5 (27.3-37.0), rigour of development 19.1 (15.3-22.2), clarity of presentation 59.3 (50.0-64.8), applicability 18.1 (13.9-25.7) and editorial independence 0.0 (0.0-0.0). The mean score in each domain of quality of Chinese diabetes CPGs was lower than that of CPGs published worldwide but higher than the mean score of Chinese guidelines of all topics. A funding source, the updated version, organisation and publishers of the guidelines and target fields are all the factors influencing the quality of CPGs to a certain degree. CONCLUSIONS: A large number of Chinese diabetes CPGs have been produced. Their quality remain unsatisfactorily low compared with CPGs worldwide, there is still room for improvement. Chinese guideline developers should pay more attention to the transparency of methodology, and use the AGREE II instrument to develop and report guidelines.
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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 | 0.129 | 0.247 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.033 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 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".