The Protocol for the Development of Iranian Clinical Practice Guideline on Dyslipidemia.
Bibliographic record
Abstract
INTRODUCTION: The prevention and control of dyslipidemia, as an important risk factor for cardiovascular diseases (CVDs), is a priority for the healthcare system to reduce the burden of these diseases. The purpose of this protocol is to outline the key steps of the first Iranian Dyslipidemia Clinical Practice Guideline development, which can be used by other researchers as a guide to design a standard, comprehensive, evidence-based, and local context-based guideline. METHOD: This guideline will be developed and reported according to the format of the World Health Organization (WHO) Handbook for Guideline Development. All members of the guideline development team will sign the declaration-of-competing-interests (DOI) forms. The development of the authors' guideline will be supported by five groups: the steering committee (SC), the Guideline Developing Group (GDG), the systematic review (evidence synthesis) group, and the external review group. The authors will also establish a patient advisory group to inform guideline development by patients' values and preferences. The SC and GDG will determine the scope of the guideline and will design PICO questions. The systematic review group will systematically search Embase, PubMed, Scopus, Web of Sciences, Cochrane Library, and Google Scholar from inception. The systematic review group will assess the risk of bias and create evidence summaries using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system. The recommendations of this guideline will be divided into strong recommendations and weak or conditional recommendations or suggestions. CONCLUSION: This clinical practice guideline will provide clinicians and healthcare professionals with new evidence-based recommendations for the diagnosis, management, and treatment of dyslipidemia in children and adults.
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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 | 0.075 | 0.185 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.107 | 0.030 |
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".