Development of a Farsi translation of the AGREE instrument, and the effects of group discussion on improving the reliability of the scores
Bibliographic record
Abstract
OBJECTIVE: We aimed to develop a formal Farsi (Persian) translation of the Appraisal of Guidelines for Research and Evaluation (AGREE) clinical guideline appraisal instrument. We considered the effect of group discussion in improving the reliability of scores. METHODS: We followed a multi-step process of translation including independent translations of the instrument and extensive assessment of face validity and fluency. We used the instruments to appraise 11 guidelines from three specialities. After the first appraisal, the raters discussed about each guideline in groups, and had the opportunity to revise their scores individually. In total 96 appraisals were conducted. The intra-class correlations (1,1) were calculated for domain scores obtained by two versions at each time point. RESULTS: We observed no statistically significant differences between the mean values obtained from the English and the translated versions of AGREE, and the scores at two time points. The average domain scores, as well as the reliability rose significantly after discussion. CONCLUSION: The Farsi version of the AGREE instrument yields in the scores comparable to the original version, despite a lower reliability. Revision of scores after group discussion leads to higher reliability, probably by helping the raters recognize what they might have overlooked during the short time of assessment.
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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 | 0.123 | 0.223 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".