Identifying actionable statements in Chinese health guidelines: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study is to validate the taxonomy and framework using Chinese guidelines and identify actionable statements. DESIGN AND SETTING: We searched five databases, to identify the health guidelines from 1 January 2020 to 1 May 2023. Five researchers categorised statements into six types: formal recommendations (Type I) with clear direction and strength, with explicit and direct evidence; good practice statements (GPS) (Type II), actionable in isolation with a significant benefit; remarks (Type III), an inseparable unit belonging to a formal recommendation or GPS that provides additional clarification; research only recommendations (Type IV) for specific populations; implementation considerations, tools and tips (Type V), that describe the how, who, where, what and when, in relation to implementing a recommendation and lacking a direct evidence link; and informal recommendations (Type VI), unrelated to evidence and not meeting GPS criteria. RESULTS: We included 116 guidelines, including 74 Western medicine guidelines, 12 traditional Chinese medicine guidelines and 30 integrated Chinese and Western medicine guidelines. 99 guidelines (85.3%) used the Grading of Recommendations Assessment, Development and Evaluation criteria. Medical specialty societies developed the highest number of guidelines (53.4%). Of all the statements, 4422 statements were extracted from the guidelines. Among them, 2154 (48.7%) were formal recommendations, 197 (4.4%) were GPS, 394 (8.9%) were remarks, 16 (0.4%) were research only recommendations, 1106 (25.0%) were implementation considerations, tools and tips, and 555 (12.6%) were informal recommendations. CONCLUSIONS: Up to date, the Chinese guideline developers tend to overestimate the number of formal recommendations and underestimate the number of GPS, remarks, research only recommendations, implementation considerations, tools and tips, and informal recommendations. Thus the current quality of actionable statements in Chinese health guidelines requires further enhancement.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.014 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".