081 The Endocrine Society Guidelines: Implications of Strong Recommendations with Low Quality Evidence
Bibliographic record
Abstract
Background In 2005, the Endocrine Society (TES) adopted the GRADE system of developing clinical practice guidelines. This system facilitates the formulation of evidence-based recommendations by explicitly describing the confidence in estimates (quality of evidence) and strength of each recommendation. Objectives To describe and characterise the relationship between confidence in estimates and strength of recommendation in TES guidelines. Methods We included all published TES guidelines between 2005 (when TES started using GRADE) and 2011. Independently and in duplicate, reviewers extracted, for each recommendation: disease area, confidence in estimates and design of the studies considered, and strength of recommendation. In strong recommendations with low quality of we developed and applied a taxonomy of appropriate recommendations and identified those we considered inappropriate. Results Most of the 357 recommendations issued were supported by evidence warranting low or very low confidence in estimates (256, 72%). Evidence cited in support of these recommendations came exclusively from observational studies in 233 recommendations (65%). Most recommendations were strong (206, 58%); of these, 121 (59%) were supported by evidence warranting low or very low confidence in estimates. In 101/121 (83%), we identified a compelling rationale for the recommendations; in 20 (17%), we did not. Conclusions Most TES strong recommendation based on low quality evidence are justified and appropriate, but a substantial proportion are not. Implications for Guideline Developers Guideline developers should carefully justify any strong recommendations based on low confidence in effect estimates.
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 | 0.251 | 0.818 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.021 | 0.016 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".