Ethnic specific recommendations in clinical practice guidelines: a first exploratory comparison between guidelines from the USA, Canada, the UK, and the Netherlands
Bibliographic record
Abstract
OBJECTIVES: To investigate whether clinical practice guidelines in different countries take ethnic differences between patients into consideration and to assess the scientific foundation of such ethnic specific recommendations. DESIGN: Analysis of the primary care sections of clinical practice guidelines. SETTING: Primary care practice guidelines for type 2 diabetes mellitus, hypertension, and asthma developed in the USA, Canada, the UK, and the Netherlands. MAIN OUTCOME MEASURES: Enumeration of the ethnic specific information and recommendations in the guidelines, and the scientific basis and strength of this evidence. RESULTS: Different guidelines do address ethnic differences between patients, but to a varying extent. The USA guidelines contained the most ethnic specific statements and the Dutch guidelines the least. Most ethnic specific statements were backed by scientific evidence, usually arising from descriptive studies or narrative reviews. CONCLUSION: The attention given to ethnic differences between patients in clinical guidelines varies between countries. Guideline developers should be aware of the potential problems of ignoring differences in ethnicity.
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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 | 0.024 | 0.195 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| 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".