P307 Guideline Development Tool (GDT) – Web-Based Solution For Guideline Developers And Authors Of Systematic Reviews
Bibliographic record
Abstract
Background Guideline developers and other health care decision makers benefit from following a structured process of specifying the health care questions they intend to answer and the outcomes of interest, assessing the confidence in the available evidence, gathering information about the values and preferences of the target population, and presentation of their results and decisions to the target users. Many guideline developers use the GRADE Profiler (GRADEpro) software used to conduct this work. Context GRADE’s approach is currently being further defined in the DECIDE (Developing and Evaluating Communication Strategies to Support Informed Decisions and Practice Based on Evidence) project. Description of Best Practice The Guideline Development Tool (GDT) is the extension of the GRADE Profiler (GRADEpro) software. The GDT provides an integrated platform-independent web-based solution for health care decision makers offering support for the whole process of making decisions and developing recommendations including question formulation, generation and prioritisation of outcomes, support for teamwork, management of potential conflicts of interest, presentation of results (including the functionality of GRADEpro) and decision support. We tested the software with individual users and in workshops as well as in guideline development processes. Lessons for Guideline Developers, Adaptors, Implementers, and/or Users Following a structured and systematic process, transparency and clarity of presentation facilitates the use of results of systematic reviews and facilitates development, updating and adaptation of evidence-based recommendations and decisions. Storing all information in a uniform, structured, transparent and annotated way also greatly facilitates updating and adaptation of systematic reviews and guidelines.
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 | 0.038 | 0.247 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.131 | 0.080 |
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".