Past, present and future of living systematic review: a bibliometrics analysis
Bibliographic record
Abstract
INTRODUCTION: In recent years, the concept of living systematic review (LSR) has attracted the attention of many scholars and institutions. A growing number of studies have been conducted based on LSR methodology, but their focus direction is unclear. The objective of this study was to provide a comprehensive review of existing LSR-related studies and to analyse their whole picture and future trends with bibliometrics. METHODS: A comprehensive search strategy was used to construct a representative dataset of LSRs up to October 2021. GraphPad V.8.2.1 and Mindmaster Pro presented the basic information of the included studies and the timeline of LSR development, respectively. The author and country cooperation network, hotspot distribution clustering, historical citation network and future development trend prediction related to LSR were visualised by VOSviewer V.1.6.16 and R-Studio V.1.4. RESULTS: A total of 213 studies were eventually included. The concept of LSR was first proposed in 2014, and the number of studies has proliferated since 2020. There was a closer collaboration between author teams and more frequent LSR research development and collaboration in Europe, North America and Australia. Numerous LSR studies have been published in high-impact journals. COVID-19 is the predominant disease of concern at this stage, and the rehabilitation of its patients and virological studies are possible directions of research in LSR for a long time to come. A review of existing studies found that more than half of the LSR series had not yet been updated and that the method needed to be more standardised in practice. CONCLUSION: Although LSR has a relatively short history, it has received much attention and currently has a high overall acceptance. The LSR methodology was further practised in COVID-19, and we look forward to seeing it applied in more areas.
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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 | 0.344 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.126 | 0.126 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".