MATCH‐IT: A decision support tool for multiple comparisons presenting data from network meta‐analysis to facilitate guideline development
Bibliographic record
Abstract
Abstract Introduction Network meta‐analysis (NMA) provides unprecedented opportunities to compare multiple treatment options (multiple comparisons) and are increasingly being used to inform clinical practice guidelines. However, the overwhelming amount of data generated from NMAs is challenging to present in comprehensible overviews for end‐users, guideline panelists included. Acknowledging these challenges, we developed MATCH‐IT—an interactive evidence summary displaying NMA results for multiple comparisons. In this study, we conducted user‐testing and further developed MATCH‐IT to support guideline panels in moving from NMA evidence to recommendations. Methods We user‐tested the tool with guideline panelists and observed the use of the tool in panel meetings. User‐testing sessions and guideline meetings were recorded, transcribed, and analyzed. The analysis informed the iterative development in the tool. Results We included four guideline panels and tested the tool with 15 panelists (four chairs, four methodologists, five clinical experts and two patient partners). User testing revealed both positive aspects and limitations of the tool. Interactivity allowed for dynamic display of the evidence during panels meetings and was highlighted as valuable. Further, participants felt that the tool provided overview of complex evidence, further facilitated by categorization of effects through colour coding. The inclusion of information on burden of treatment was highlighted as relevant and valuable. Regarding limitations, some users had issues discovering the interactive features. Earlier versions of MATCH‐IT did not include sufficiently detailed information, such as the imprecision of effect estimates, which the users felt was needed for decision making. These findings led to refinements of the tool, including a new tutorial, inclusion of confidence intervals, and a new layer displaying more detailed information. Discussion Our study suggests that MATCH‐IT may play a role in facilitating guideline development by easing understanding of NMA evidence and alleviating information overload in guideline panelists.
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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 | 0.332 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".