A Model for Developing Subspecialty Clinical Practice Guidelines: The Geriatric Emergency Department Guidelines 2.0
Bibliographic record
Abstract
The original consensus-based Geriatric Emergency Department (GED) Guidelines, published in 2014, established a framework of core principles for delivering high-quality, age-appropriate emergency care for older adults. In response to significant advances in geriatric emergency medicine research and evolving clinical priorities, we developed the GED Guidelines 2.0 to ensure continued relevance, clinical utility, and evidence-based rigor. This concept paper describes the systematic and iterative process undertaken to update the guidelines, including the formation of multidisciplinary working groups and the application of the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology. Unlike the original GED Guidelines, our approach prioritized methodological transparency, formalized evidence grading, and consensus building grounded in systematic reviews and meta-analyses. We describe the identification, recruitment, and collaboration of multidisciplinary clinical and academic experts working together to improve the care of older adults in the emergency department. Through this multidisciplinary effort, key geriatric domains were selected, priority topics identified, and systematic reviews and meta-analyses conducted to generate a robust evidence base for future guideline and policy development. The GED Guidelines 2.0 represents the first emergency medicine (EM) subspecialty guideline effort to fully adopt the GRADE framework, offering a novel blueprint for future EM guideline development.
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 | 0.184 | 0.317 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.010 | 0.011 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".