Summarizing systematic reviews
Bibliographic record
Abstract
AIMS: With the increase in the number of systematic reviews available, a logical next step to provide decision makers in healthcare with the evidence they require has been the conduct of reviews of existing systematic reviews. Syntheses of existing systematic reviews are referred to by many different names, one of which is an umbrella review. An umbrella review allows the findings of reviews relevant to a review question to be compared and contrasted. An umbrella review's most characteristic feature is that this type of evidence synthesis only considers for inclusion the highest level of evidence, namely other systematic reviews and meta-analyses. A methodology working group was formed by the Joanna Briggs Institute to develop methodological guidance for the conduct of an umbrella review, including diverse types of evidence, both quantitative and qualitative. The aim of this study is to describe the development and guidance for the conduct of an umbrella review. METHODS: Discussion and testing of the elements of methods for the conduct of an umbrella review were held over a 6-month period by members of a methodology working group. The working group comprised six participants who corresponded via teleconference, e-mail and face-to-face meeting during this development period. In October 2013, the methodology was presented in a workshop at the Joanna Briggs Institute Convention. Workshop participants, review authors and methodologists provided further testing, critique and feedback on the proposed methodology. RESULTS: This study describes the methodology and methods developed for the conduct of an umbrella review that includes published systematic reviews and meta-analyses as the analytical unit of the review. Details are provided regarding the essential elements of an umbrella review, including presentation of the review question in a Population, Intervention, Comparator, Outcome format, nuances of the inclusion criteria and search strategy. A critical appraisal tool with 10 questions to help assess risk of bias in systematic reviews and meta-analyses was also developed and tested. Relevant details to extract from included reviews and how to best present the findings of both quantitative and qualitative systematic reviews in a reader friendly format are provided. CONCLUSIONS: Umbrella reviews provide a ready means for decision makers in healthcare to gain a clear understanding of a broad topic area. The umbrella review methodology described here is the first to consider reviews that report other than quantitative evidence derived from randomized controlled trials. The methodology includes an easy to use and informative summary of evidence table to readily provide decision makers with the available, highest level of evidence relevant to the question posed.
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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 | 0.137 | 0.363 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.061 | 0.052 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.050 | 0.027 |
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".