077 Development of an Evidence to Recommendation Table for Guideline Users
Bibliographic record
Abstract
Background The DECIDE project aims to improve the dissemination of recommendations using GRADE. Clinical practice guidelines (CPGs) summary tables do not include all the relevant factors for moving from evidence to recommendations: quality of the evidence, balance of benefits and harms, values and preferences and cost. Objectives Development of an optimal presentation table to inform about the evidence to recommendation (EtR) process to healthcare professionals. Methods Iterative process including brainstorming and design, user testing with semi-structured interviews, and stakeholder consultation. We analysed the feedback to our initial prototype, defined barriers and facilitators, and generated alternative formats. Results The table was well rated overall by users. It was found useful to understand in more depth the rationale of the recommendation and of use for teaching sessions. Some users found it potentially useful for shared decision making while others did not find it useful at the point of care. Most frustrations came from misunderstanding some terms, the general purpose of the table or the GRADE system. Discussion This EtR table could be a useful tool for CPGs users and tabulates all the relevant information beneath the EtR process. We are preparing a second round of user testing and stakeholder consultation, and will present a new format at the conference. Implications for GL Developers and Users This table will provide users a concise summary of the factors influencing the EtR process. The efficiency of including an EtR table in real CPGs needs to be further evaluated.
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 | 0.128 | 0.420 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.053 | 0.027 |
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".