EPOS2020: development strategy and goals for the latest European Position Paper on Rhinosinusitis
Bibliographic record
Abstract
Background: The European Position Papers on Rhinosinusitis from 2005, 2007 and 2012 have had a measurable impact on the way this common condition with high impact on quality of life is managed around the world.EPOS2020 will be the latest iteration of the guideline, addressing new stakeholders and target users, presenting a summary of the latest literature and evolving treatment modalities, and formulating clear recommendations based on all available evidence. Methodology:Based on the AGREE II framework, this article demonstrates how the EPOS2020 steering group will address six key areas to ensure consistency in quality and presentation of information in the latest rhinosinusitis clinical practice guideline: scope and purpose; stakeholder involvement; rigour of development; clarity of presentation; recommendations and applicability; editorial independence.Results: By analysing the guidance from AGREE II, we formulated a detailed development strategy for EPOS2020.We identify new stakeholders and target users and ratify the importance of patient involvement in the latest EPOS guideline.New and expanded areas of research to be addressed are highlighted.We confirm our intention to use mixed methodologies, combining evidencebased medicine with real life studies; when no evidence can be found, use Delphi rounds to achieve clear, inclusive recommendations.We also introduce new concepts for dissemination of the guideline, using Internet and social media to improve accessibility.Conclusion: This article is an introduction to the EPOS2020 project, and presents the key goals, core stakeholders, planned methodology and dissemination strategies for the latest version of this influential guideline.
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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 | 0.096 | 0.086 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.017 | 0.007 |
| Insufficient payload (model declined to judge) | 0.033 | 0.036 |
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".