Comparison between two tools assessing the methodological quality of systematic reviews: ReMarQ and AMSTAR 2
Bibliographic record
Abstract
Abstract Several tools are available for assessing the methodological quality of systematic reviews. The ReMarQ tool – centred on the assessment of the reporting methodological quality of systematic reviews – comprises 26 dichotomous items and does not require clinical or background knowledge of the review topic for its application. In this study, we aimed to compare the results of evaluating the methodological quality of systematic reviews using ReMarQ and A MeaSurement Tool to Assess systematic Reviews (AMSTAR) 2. We assessed a sample of randomly selected systematic reviews published in medical journals using ReMarQ and AMSTAR 2. We calculated the correlation and agreement between the number of fulfilled items in ReMarQ and the number of (i) fulfilled and (ii) fulfilled or partially fulfilled items according to AMSTAR 2. We assessed 51 systematic reviews using both tools. The number of fulfilled items in ReMarQ was strongly correlated with the number of fulfilled items ( = 0.79; 95%CI = 0.65;0.87) and the number of fulfilled or partially fulfilled items ( = 0.85; 95%CI = 0.74;0.90) in AMSTAR 2. The percentage of fulfilled ReMarQ items displayed a high agreement with the percentage of fulfilled or partially fulfilled AMSTAR items. In conclusion, the number of fulfilled items in ReMarQ is strongly correlated with that in AMSTAR 2 and there is good agreement between these two tools on the percentage of fulfilled items.
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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 | 0.417 | 0.666 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.019 | 0.043 |
| Bibliometrics | 0.036 | 0.025 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".