A Primer on Systematic Review and Meta-analysis in Diabetes Research
Bibliographic record
Abstract
A systematic review is a rigorous process that involves identifying, selecting, and synthesizing available evidence pertaining to an a priori-defined research question. The resulting evidence base may be summarized qualitatively or through a quantitative analytic approach known as meta-analysis. Systematic review and meta-analysis (SRMAs) have risen in popularity across the scientific realm including diabetes research. Although well-conducted SRMAs are an indispensable tool in informing evidence-based medicine, the proliferation of SRMAs has led to many reviews of questionable quality and misleading conclusions. The objective of this article is to provide up-to-date knowledge and a comprehensive understanding of strengths and limitations of SRMAs. We first provide an overview of the SRMA process and offer ways to identify common pitfalls at key steps. We then describe best practices as well as evolving approaches to mitigate biases, improve transparency, and enhance rigor. We discuss several recent developments in SRMAs including individual-level meta-analyses, network meta-analyses, umbrella reviews, and prospective meta-analyses. Additionally, we outline several strategies that can be used to enhance quality of SRMAs and present key questions that authors, editors, and readers should consider in preparing or critically reviewing SRMAs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.163 | 0.313 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.022 | 0.026 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.015 | 0.029 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".