Development of the AGREE II, part 2: assessment of validity of items and tools to support application
Bibliographic record
Abstract
BACKGROUND: We established a program of research to improve the development, reporting and evaluation of practice guidelines. We assessed the construct validity of the items and user's manual in the beta version of the AGREE II. METHODS: We designed guideline excerpts reflecting high-and low-quality guideline content for 21 of the 23 items in the tool. We designed two study packages so that one low-quality and one high-quality version of each item were randomly assigned to each package. We randomly assigned 30 participants to one of the two packages. Participants reviewed and rated the guideline content according to the instructions of the user's manual and completed a survey assessing the manual. RESULTS: In all cases, content designed to be of high quality was rated higher than low-quality content; in 18 of 21 cases, the differences were significant (p < 0.05). The manual was rated by participants as appropriate, easy to use, and helpful in differentiating guidelines of varying quality, with all scores above the mid-point of the seven-point scale. Considerable feedback was offered on how the items and manual of the beta-AGREE II could be improved. INTERPRETATION: The validity of the items was established and the user's manual was rated as highly useful by users. We used these results and those of our study presented in part 1 to modify the items and user's manual. We recommend AGREE II (available at www.agreetrust.org) as the revised standard for guideline development, reporting and evaluation.
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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 | 0.170 | 0.337 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".