Making the AGREE tool more user‐friendly: the feasibility of a user guide based on Boolean operators
Bibliographic record
Abstract
Rationale, aims and objectives The Appraisal of Guidelines Research and Evaluation (AGREE) instrument is a generic tool for assessing guideline quality. This feasibility study aimed to reduce the ambiguity and subjectivity associated with AGREE item scoring, and to augment the tool's capacity to differentiate between good- and poor-quality guidelines. Methods A literature review was conducted to ascertain what AGREE instrument adjustments had been reported to date. The AGREE User Guide was then modified by: 1 constructing a detailed set of instructions, or dictionary, using Boolean operators, and 2 overlaying seven criteria to categorize guideline quality. The feasibility of the Boolean-based dictionary was tested by three appraisers using three randomly selected guidelines on low back pain management. The dictionary was then revised and re-tested. Results Of the 52 published studies identified, 14% had modified the instrument by adding or deleting items and 35% had adopted strategies, such as using a consensus approach, to overcome inconsistencies and ensure identical item scoring among appraisers. For the feasibility test, Pearson correlation coefficients ranged from 0.27 to 0.81. Revision and re-testing of the dictionary increased the level of agreement (range 0.41 to 0.94). Application of the revised dictionary not only decreased the variability of the domain scores, but also reduced the tool's reliability among inexperienced appraisers. Conclusion Appraisers found the Boolean-based AGREE User Guide easier to use than the original, which improved their confidence in the tool. Good reliability was achieved in the feasibility test, but the reliability and validity of some of the changes will require further evaluation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.127 | 0.526 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads 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".