313WS Electronic multilayered guideline format: a novel structure and presentation of trustworthy guidelines at the point of care
Bibliographic record
Abstract
Background The DECIDE Project – created by the GRADE Working Group and funded by the European Union – aims at developing and evaluating strategies to improve dissemination and uptake of evidence-based recommendations. Work Package I targets health care professionals and has developed an electronic multilayered guideline format that includes the top layer; consisting of the minimum set of information components deemed necessary for clinicians to act on a recommendation. The first phase of iterative refinements through stakeholder feedback and user testing is completed and we’re now initiating the second phase consisting of surveys and randomised trials of alternative formats. Objectives To update participants on the DECIDE project (WP1) and gather feedback on current and alternative guideline formats. Target Group Guideline developers. Description The workshop will open with an introduction to the background and progress of the DECIDE project/WP1. Participants will be given a clinical scenario together with relevant examples of guidelines after which they’re asked to provide anonymous information on attitudes and perceptions of trustworthy guidelines, the use of GRADE and current presentation formats. Following this they’ll be given a systematic review on the same subject and asked to write a draft recommendation in the top layer format using a prototype online authoring tool tailored for the format. Participants will at the end of the session be asked to provide feedback on the novel format, specifically on relevance, comprehension, likability and feasibility of production. Feedback is collected using a multiple-choice survey with clickers in addition to a final discussion
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 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.009 |
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".