Updating Systematic Reviews: An International Survey
Bibliographic record
Abstract
BACKGROUND: Systematic reviews (SRs) should be up to date to maintain their importance in informing healthcare policy and practice. However, little guidance is available about when and how to update SRs. Moreover, the updating policies and practices of organizations that commission or produce SRs are unclear. METHODOLOGY/PRINCIPAL FINDINGS: The objective was to describe the updating practices and policies of agencies that sponsor or conduct SRs. An Internet-based survey was administered to a purposive non-random sample of 195 healthcare organizations within the international SR community. Survey results were analyzed using descriptive statistics. The completed response rate was 58% (n = 114) from across 26 countries with 70% (75/107) of participants identified as producers of SRs. Among responders, 79% (84/107) characterized the importance of updating as high or very-high and 57% (60/106) of organizations reported to have a formal policy for updating. However, only 29% (35/106) of organizations made reference to a written policy document. Several groups (62/105; 59%) reported updating practices as irregular, and over half (53/103) of organizational respondents estimated that more than 50% of their respective SRs were likely out of date. Authors of the original SR (42/106; 40%) were most often deemed responsible for ensuring SRs were current. Barriers to updating included resource constraints, reviewer motivation, lack of academic credit, and limited publishing formats. Most respondents (70/100; 70%) indicated that they supported centralization of updating efforts across institutions or agencies. Furthermore, 84% (83/99) of respondents indicated they favoured the development of a central registry of SRs, analogous to efforts within the clinical trials community. CONCLUSIONS/SIGNIFICANCE: Most organizations that sponsor and/or carry out SRs consider updating important. Despite this recognition, updating practices are not regular, and many organizations lack a formal written policy for updating SRs. This research marks the first baseline data available on updating from an organizational perspective.
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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 | 0.099 | 0.257 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.019 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".