How to Conduct a Systematic Review and Meta-Analysis: A Guide for Clinicians
Bibliographic record
Abstract
Evidence-based practice relies on using research evidence to guide clinical decision-making. However, staying current with all published research can be challenging. Many clinicians use review articles that apply predefined methods to locate, identify, and summarize all available evidence on a topic to guide clinical decision-making. This paper discusses the role of review articles, including narrative, scoping, and systematic reviews, to synthesize existing evidence and generate new knowledge. It provides a step-by-step guide to conducting a systematic review and meta-analysis, covering key steps such as formulating a research question, selecting studies, evaluating evidence quality, and reporting results. This paper is intended as a resource for clinicians looking to learn how to conduct systematic reviews and advance evidence-based practice in the field.
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 | 0.226 | 0.439 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.023 | 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; 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".