Reporting, presentation and wording of recommendations in clinical practice guideline for gout: a systematic analysis
Bibliographic record
Abstract
OBJECTIVES: We systematically analysed recommendations from gout guidelines as an example, to provide a basis for developing a reporting standard of recommendations in clinical practice guidelines (CPGs). DESIGN: Systematic review without meta-analysis. METHODS: We systematically searched MEDLINE and all relevant guideline websites (National Institute for Health and Care Excellence, National Guideline Clearinghouse, Scottish Intercollegiate Guidelines Network, WHO, Guidelines International Network, DynaMed, UpTodate, Best Practice) from their inception to January 2017 to identify and select gout CPGs. We used search terms such as 'gout', 'hyperuricemia' and 'guideline'. We included the eligible CPGs of gout according to the predefined inclusion and exclusion criteria after screening titles, abstracts and full texts. The characteristics of recommendations reported in the included guidelines were extracted and analysed. RESULTS: A total of 15 gout guidelines with a range of 5-80 recommendations were retrieved. Several indicators were used in the gout guidelines to facilitate identification of recommendations, including grouping all recommendations in a summary section, formatting recommendations in a particular or special way, using locating words for recommendations and indicating the strength of recommendation and quality of evidence. We found some components commonly used in the recommendations. The wording of recommendations varied across guidelines. Recommendations were detailed and explained in the section of rationale and explanation of recommendations. In some guidelines, recommendations were accompanied with other material to assist their reporting. CONCLUSIONS: Variability and inconsistency were found on the reporting and presentation of recommendations in gout guidelines. Several points for reporting recommendation can be summarised. First, we suggested summarising and highlighting the core recommendations in a guideline. Second, guideline developers should try to structure and write recommendations reasonably. Third, it was necessary to detail and explain the recommendations and their rationale. Finally, describing and providing other potential useful contents was also a helpful way for clear reporting.
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 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 | 0.019 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".