Guidelines do not self-implement: time for a research paradigm shift from massive creation to effective implementation in evidence-based medicine research in China
Bibliographic record
Abstract
> Evidence-based medicine should be complemented by evidence-based implementation. > > —Grol, R. and Grimshaw, J. (1999) In 2018, the BMJ opened a special collection, analysing the evolution of medical research in China, with a paper entitled ‘Clinical practice guidelines in China’.1 Chen et al ’s paper1 described the publication growth, low methodological quality, potential conflict of interest and poor implementation status of clinical practice guidelines (CPGs) in China, and offered five recommendations for Chinese CPG development and implementation. As researchers working in the field of implementation science, we feel that the paper’s aim was not fully realised due to the lack of discussion on guideline implementation. We argue that although high-quality guideline development is essential, researchers need to simultaneously focus on how to improve guideline implementation, especially when high-quality guidelines already exist and can be adopted as it is or can be adapted for the local context. It is time for Chinese evidence-based medicine (EBM) researchers and stakeholders to embrace and advance implementation science, answering questions on how guideline implementation can be optimised in varying contexts. Chen et al ’s paper, taken as a whole, seems to imply that the mere existence of high-quality CPGs leads to …
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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 | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
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 | 0.155 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.017 | 0.031 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".