Towards evidence-based clinical practice: an international survey of 18 clinical guideline programs
Bibliographic record
Abstract
OBJECTIVE: To describe systematically the structures and working methods of guideline programs. DESIGN: Descriptive survey using a questionnaire with 32 items based on a framework derived from the literature. Answers were tabulated and checked by participants. STUDY PARTICIPANTS: Key informants of 18 prominent guideline organizations in the United States, Canada, Australia, New Zealand, and nine European countries. MAIN OUTCOME MEASURES: History, aims, methodology, products and deliveries, implementation, evaluation, procedure for updating guidelines, and future plans. RESULTS: Most guideline programs were established to improve the quality and effectiveness of health care. Most use electronic databases to collect evidence and systematic reviews to analyze the evidence. Consensus procedures are used when evidence is lacking. All guidelines are reviewed before publication. Authorization is commonly used to endorse guidelines. All guidelines are furnished with tools for application and the Internet is widely used for dissemination. Implementation strategies vary among different organizations, with larger organizations leaving this to local organizations. Almost all have a quality assurance system for their programs. Half of the programs do not have formal update procedures. CONCLUSIONS: Principles of evidence-based medicine dominate current guideline programs. Recent programs are benefiting from the methodology created by long-standing programs. Differences are found in the emphasis on dissemination and implementation, probably due to differences in health care systems and political and cultural factors. International collaboration should be encouraged to improve guideline methodology and to globalize the collection and analysis of evidence needed for guideline development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".