Guidelines do not self-implement: time for a research paradigm shift from massive creation to effective implementation in evidence-based medicine research in China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 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 | 0.065 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it