Developing methodology for the creation of clinical practice guidelines for rare diseases: A report from RARE-Bestpractices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Rare diseases are a global public health priority; they can cause significant morbidity and mortality, can gravely affect quality of life, and can confer a social and economic burden on families and communities. These conditions are, by their nature, encountered very infrequently by clinicians. Thus, clinical practice guidelines are potentially very helpful in supporting clinical decisions, health policy and resource allocation. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) system is a structured and transparent approach to developing and presenting summaries of evidence, grading its quality, and then transparently interpreting the available evidence to make recommendations in health care. GRADE has been adopted widely. However, its use in creating guidelines for rare diseases – which are often plagued by a paucity of high quality evidence – has not yet been explored. RARE-Bestpractices is a project to create and populate a platform for sharing best practices for management of rare diseases. A major aim of this project is to ensure that European Union countries have the capacity to produce high quality clinical practice guidelines for rare diseases. On February 12, 2013 at the Istituto Superiore di Sanità, in Rome, Italy, the RARE-Bestpractices group held the first of a series of 2 workshops to discuss methodology for creating clinical practice guidelines, and explore issues specific to rare diseases. This paper summarizes key results of the first workshop, and explores how the current GRADE approach might (or might not) work for rare diseases. Avenues for future research are also identified.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.511 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it