GRADE Notes 4: how to use GRADE when there is “no” evidence? A case study of using unpublished registry data
Bibliographic record
Abstract
OBJECTIVES: Trustworthy guidelines rely on systematic reviews of the best available published evidence. The GRADE (Grading of Recommendations Assessment, Development, and Evaluation) Working Group has provided guidance about developing evidence-based recommendations when published direct evidence is lacking. In this article, we provide a case example as an alternate solution to generate primary data using registries prior to collecting expert evidence. STUDY DESIGN AND SETTING: When direct published literature was absent, a team of clinical and statistical expertise can utilize registries, when available, for primary data generation in a way that allows for answering clinically important questions. RESULTS: Out of 54 questions prioritized by a guideline development for the prevention and management of peritoneal dialysis-associated infections in children, 25 questions had no evidence to inform them. The use of unpublished registry data served as a primary source of information to answer 12 of the 25 questions and provided additional information for nine questions for which at least one published study was available. CONCLUSION: This article extends our previous GRADE note for scenarios of "no" evidence, highlighting the value of generating primary evidence using unpublished registry data when relevant registries and resources allow. This approach can be of particular value when addressing conditions that are rare or from populations that are considered vulnerable, while emphasizing the importance of being transparent regarding the reporting of raw data and the analysis plan in the event of reporting unpublished work.
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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 | 0.240 | 0.688 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.024 | 0.012 |
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".