Making GRADE accessible: a proposal for graphic display of evidence quality assessments
Bibliographic record
Abstract
When generating guidelines, quality of evidence is frequently reported in tabulated form capturing several domains, for example, study design, risk of bias and heterogeneity. Increasingly, this is done using the Grading of Recommendations Assessment, Development and Evaluation approach. As assimilating large amount of tabulated data across several comparisons and outcomes spread over many pages (sometimes hundreds) is not easy, there is a need to present evidence summaries in a more effective way. A graphic display plotting the several domains used in evidence grading on equiangular spokes starting from the same point, the data length of each spoke proportional to the magnitude of the quality, succinctly captures tabulated information. These plots allow easy identification of deficiencies, outliers and similarities in evidence quality for individual and multiple comparisons and outcomes, paving the way for their routine use alongside tabulated information.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Reporting · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
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 | 0.260 | 0.594 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.006 | 0.012 |
| Bibliometrics | 0.037 | 0.034 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.028 | 0.032 |
| Open science | 0.016 | 0.019 |
| Research integrity | 0.018 | 0.026 |
| Insufficient payload (model declined to judge) | 0.056 | 0.048 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".