The AGREE Enterprise: a decade of advancing clinical practice guidelines
Bibliographic record
Abstract
BACKGROUND: The original AGREE (Appraisal of Guidelines for REsearch and Evaluation) Instrument was published in 2003, and its revision, the AGREE II, in 2009. Together, they filled an important gap in the guideline and quality of care fields. Ten years later, the AGREE Enterprise reflects on a trajectory of projects and international collaboration that have contributed to advancing the science and quality of practice guidelines and the uptake of AGREE/AGREE II. FINDINGS: The AGREE Enterprise has undertaken activities to improve the tool and to develop resources to support its use. Since 2003, the uptake and adoption of AGREE by the international community has been swift and broad. A total of 33 language translations of the original AGREE Instrument and the current AGREE II are available and were initiated by the international community. A recent scan of the published literature identified over 600 articles that referenced the AGREE tools. The AGREE tools have been widely received and applied, with several organizations having incorporated the AGREE as part of their formal practice guideline programs. Since its redevelopment in 2010, the AGREE Enterprise website (www.agreetrust.org) continues to experience steady increases in visitors per month and currently has over 10,000 registered users. CONCLUSIONS: The AGREE Enterprise has contributed to the advancements of guidelines through research activities and international participation by scientific and user communities. As we enter a new decade, we look forward to ongoing collaborations and contributing to further advancements to improve quality of care and health care systems.
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 | 0.434 | 0.481 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.018 | 0.031 |
| Open science | 0.008 | 0.022 |
| Research integrity | 0.016 | 0.028 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".