Knowledge user survey and Delphi process to inform development of a new risk of bias tool to assess systematic reviews with network meta-analysis (RoB NMA tool)
Bibliographic record
Abstract
Background Network meta-analysis (NMA) is increasingly used in guideline development and other aspects of evidence-based decision-making. We aimed to develop a risk of bias (RoB) tool to assess NMAs (RoB NMA tool). An international steering committee recommended that the RoB NMA tool to be used in combination with the Risk of Bias in Systematic reviews (ROBIS) tool (i.e. because it was designed to assess biases only) or other similar quality appraisal tools (eg, A MeaSurement Tool to Assess systematic Reviews 2 [AMSTAR 2]) to assess quality of systematic reviews. The RoB NMA tool will assess NMA biases and limitations regarding how the analysis was planned, data were analysed and results were presented, including the way in which the evidence was assembled and interpreted. Objectives Conduct (a) a Delphi process to determine expert opinion on an item’s inclusion and (b) a knowledge user survey to widen its impact. Design Cross-sectional survey and Delphi process. Methods Delphi panellists were asked to rate whether items should be included. All agreed-upon item were included in a second round of the survey (defined as 70% agreement). We surveyed knowledge users’ views and preferences about the importance, utility and willingness to use the RoB NMA tool to evaluate evidence in practice and in policymaking. We included 12 closed and 10 open-ended questions, and we followed a knowledge translation plan to disseminate the survey through social media and professional networks. Results 22 items were entered into a Delphi survey of which 28 respondents completed round 1, and 22 completed round 2. Seven items did not reach consensus in round 2. A total of 298 knowledge users participated in the survey (14% respondent rate). 75% indicated that their organisation produced NMAs, and 78% showed high interest in the tool, especially if they had received adequate training (84%). Most knowledge users and Delphi panellists preferred a tool to assess both bias in individual NMA results and authors’ conclusions. Response bias in our sample is a major limitation as knowledge users working in high-income countries were more represented. One of the limitations of the Delphi process is that it depends on the purposive selection of experts and their availability, thus limiting the variability in perspectives and scientific disciplines. Conclusions This Delphi process and knowledge user survey informs the development of the RoB NMA tool.
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 | 0.506 | 0.594 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.018 | 0.011 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".