A systematic survey of 200 systematic reviews with network meta-analysis (published 2020–2021) reveals that few reviews report structured evidence summaries
Bibliographic record
Abstract
OBJECTIVES: To map whether and how systematic reviews (SRs) with network meta-analysis (NMA) use presentation formats to report (a) structured evidence summaries - here defined as reporting of effects estimates in absolute effects with certainty ratings and with a method to rate interventions across one or more outcome(s) - and (b) NMA results in general. STUDY DESIGN AND SETTING: We conducted a systematic survey, searching MEDLINE (Ovid) for SRs with NMA published between January 1, 2020, and December 31, 2021. We planned to include a random sample of publications, with predefined mechanisms in place for saturation, and included SRs that met prespecified quality criteria and extracted data on presentation formats that reported: (a) estimates of effects, (b) certainty of the evidence, or (c) rating of interventions. RESULTS: The 200 eligible SRs, from 158 unique Journals, utilized 1133 presentation formats. We found structured evidence summaries in 10 publications (5.0%), with 3 (1.5%) reporting structured evidence summaries across all outcomes, including benefits and harms. Sixteen of the 133 SRs (11.7%) reporting dichotomous outcomes included estimates of absolute effects. Seventy-six SRs (38.0%) reported both benefits and harms and 26 SRs (13.0%) reported certainty ratings in presentation formats, 20 (76.9%) used Grading of Recommendations Assessment, Development and Evaluation and 6 (23.1%) used Confidence In Network Meta-analysis. Surface Under the Cumulative Ranking Curve was the most common method to rate interventions (69 SRs, 34.5%). NMA results were most often reported using forest plots (108 SRs, 54.0%) and league tables (93 SRs, 46.5%). CONCLUSION: Most SRs with NMA do not report structured evidence summaries and only rarely do such summaries include reporting of both benefits and harms; those that do offer effective user-friendly communication and provide models for optimal NMA presentation practice.
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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 | 0.154 | 0.549 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.014 |
| Bibliometrics | 0.052 | 0.061 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".