Enhancing the Trustworthiness of the Endocrine Society’s Clinical Practice Guidelines
Bibliographic record
Abstract
In an effort to enhance the trustworthiness of its clinical practice guidelines, the Endocrine Society has recently adopted new policies and more rigorous methodologies for its guideline program. In this Clinical Practice Guideline Communication, we describe these recent enhancements-many of which reflect greater adherence to the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach to guideline development-in addition to the rationale for such changes. Improvements to the Society's guideline development practices include, but are not limited to, enhanced inclusion of nonendocrinologist experts, including patient representatives, on guideline development panels; implementation of a more rigorous conflict/duality of interest policy; a requirement that all formal recommendations must be demonstrably underpinned by systematic evidence review; the explicit use of GRADE Evidence-to-Decision frameworks; greater use and explanation of standardized guideline language; and a more intentional approach to guideline updating. Lastly, we describe some of the experiential differences our guideline readers are most likely to notice.
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 | 0.679 | 0.908 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.011 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.025 | 0.019 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.014 | 0.025 |
| Insufficient payload (model declined to judge) | 0.004 | 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".