The development of clinical guidelines in China: insights from a national survey
Bibliographic record
Abstract
BACKGROUND: Previous research suggests that the quality of clinical guidelines (CGs) in China is suboptimal. However, little is known about the methodology that CGs follow. We conducted a national survey of methods used by Chinese CG developers for CG development, adaptation, and updating. METHODS: We used a previously piloted questionnaire based on methodologies of CG development, adaptation, and updating, which was distributed during September-November 2020 to 114 organizations identified from published Chinese CGs (searched 2017-2020), recommended by Chinese CG developers, and recommended by clinical discipline experts. RESULTS: We collected 48 completed questionnaires (42.1% response). Most organizations developed CGs based on scientific evidence (89.6%), existing CGs (75%), or expert experience and opinion (64.6%). Only a few organizations had a specific CG development division (6.3%), a CG monitoring plan (on clinicians 33.3%; on patients 18.8%), funding (33.3%), or a conflict-of-interest (COI) management policy (23.4%). Thirty (62.5%) organizations reported using a CG development methodology handbook, from international organizations (14/30, 46.7%), methodology or evaluation resources (3/30, 10.0%), expert experience and opinion (3/30, 10.0%), or in-house handbooks (3/30, 10.0%). One organization followed a published adaptation methodology. Thirty-eight organizations (88.4%) reported de novo CG development: 21 (55.3%) formed a CG working group, and 29 (76.3%) evaluated the quality of evidence (21 [72.4%] using a methodological tool). Nineteen organizations (52.8%) reported CG adaptation: three (31.6%) had an adaptation working group, and 12 (63.2%) evaluated the quality of source CGs (2 (16.7%) using the AGREE II instrument). Thirty-three organizations (68.8%) updated their CGs, seven (17.5%) using a formal updating process. CONCLUSIONS: Our study describes how CGs are developed in a middle-income country like China. To ensure better healthcare, there is still an important need for improvement in the development, adaptation, and updating of CG in China.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.153 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".