A Case for Clarity, Consistency, and Helpfulness: State-of-the-Art Clinical Practice Guidelines in Endocrinology Using the Grading of Recommendations, Assessment, Development, and Evaluation System
Bibliographic record
Abstract
Context: The Endocrine Society, and a growing number of other organizations, have adopted the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) system to develop clinical practice guidelines and grade the strength of recommendations and the quality of the evidence. Despite the use of GRADE in several of The Endocrine Society’s clinical practice guidelines, endocrinologists have not had access to a context-specific discussion of this system and its merits. Evidence Acquisition: The authors are involved in the development of the GRADE standard and its application to The Endocrine Society clinical practice guidelines. Examples were extracted from these guidelines to illustrate how this grading system enhances the quality of practice guidelines. Evidence Synthesis: We summarized and described the components of the GRADE system, and discussed the features of GRADE that help bring clarity and consistency to guideline documents, making them more helpful to practicing clinicians and their patients with endocrine disorders. Conclusions: GRADE describes the quality of the evidence using four levels: very low, low, moderate, and high quality. Recommendations can be either strong (“we recommend”) or weak (“we suggest”), and this strength reflects the confidence that guideline panel members have that patients who receive recommended care will be better off. The separation of the quality of the evidence from the strength of the recommendation recognizes the role that values and preferences, as well as clinical and social circumstances, play in formulating practice recommendations.
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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 | 0.597 | 0.840 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.020 | 0.013 |
| Science and technology studies | 0.008 | 0.040 |
| Scholarly communication | 0.040 | 0.048 |
| Open science | 0.012 | 0.021 |
| Research integrity | 0.029 | 0.047 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".