Visual transformation for guidelines presentation of the strength of recommendations and the certainty of evidence
Bibliographic record
Abstract
OBJECTIVE: The objective of this paper is to propose an approach to visual unification of adapted guidelines and transformation of classifications of certainty of evidence (CoE) and strength of recommendations (SoR) into the approach suggested by the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) working group. STUDY DESIGN AND SETTING: We carried out a literature search in MEDLINE and Epistemonikos, an analysis of selected guidelines, and an iterative discussion to decide on a consistent visual presentation and CoE and SoR depictions. RESULTS: The results of the literature search suggested this issue had not been addressed yet. The analysis of the chosen eight guidelines showed significant heterogeneity in the visual presentation of recommendations. Recommendations were often worded similarly to whether or not they were strong or conditional. Many guidelines contained "statements," almost all of which did not fulfill the good practice statement (GPS) criteria. We proposed an approach for transforming recommendations that are being adapted and which use various classification systems for CoE and SoR into GRADE and a consistent visual style. CONCLUSION: Guideline developers should aim for unification in the formulation of recommendations to improve transferability.
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 | 0.247 | 0.520 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.032 | 0.015 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".