When and how to update systematic reviews
Bibliographic record
Abstract
Governments, funding agencies, academic institutions, and health care policy makers are increasingly investing in the design, development, and dissemination of systematic reviews (SRs) to inform clinical practice guidelines, ethical guidance of clinical research, and health care practice and policy. SRs need to be sensitive to the dynamic nature of new evidence, such as published papers. The emergence of new evidence over time may undermine the validity of conclusions and recommendations in any given SR and subsequent practice guideline. This issue has only started to be more seriously considered during the last decade or so. Now it is clear that the use of out-dated evidence can lead to a waste of resources, provision of redundant, ineffective or even harmful health care. \n \nThe author of this dissertation and his colleagues conducted and published three empirical studies and two conceptual articles (in six peer-reviewed journal publications), which addressed the methodologic aspects of when and how to update SRs. This PhD project provides a summary of these publications. The work described herein has had a significant impact on raising awareness and initiating new research efforts for keeping SRs up-to-date. \n \nPublication 1 proposed the first formal definition of what constitutes an update of a SR. The article presented distinguishing features of an updated vs. not updated or a new review. Publication 2 (or Publication 3) systematically reviewed methods, techniques, and strategies describing when and how to update SRs (Study #1). Publication 4, an international survey (Study #2), identified and described updating practices and policies of organisations involved in the production and commission of SRs. Publication 5 reviewed the knowledge and efforts in updating SRs and provided guidance for authors and SR groups as to when and how to update comparative effectiveness reviews produced by the Agency for Healthcare Research and Quality’s (AHRQ) Evidence-based Practice Centres (EPCs) throughout North America. Publication 6 (Study #3) described the development, piloting, and feasibility of a surveillance system to assess the need for updating comparative effectiveness reviews produced by the AHRQ’s EPC Program. This surveillance method has proved to be an efficient approach for prioritising SRs with respect to updating need.
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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 | 0.536 | 0.846 |
| Meta-epidemiology (narrow) | 0.008 | 0.014 |
| Meta-epidemiology (broad) | 0.019 | 0.016 |
| Bibliometrics | 0.050 | 0.029 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.047 | 0.068 |
| Open science | 0.018 | 0.021 |
| Research integrity | 0.035 | 0.027 |
| Insufficient payload (model declined to judge) | 0.020 | 0.018 |
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".