Systematic reviews of the literature: an introduction to current methods
Bibliographic record
Abstract
Systematic reviews are a type of evidence synthesis in which authors develop explicit eligibility criteria, collect all the available studies that meet these criteria, and summarize results using reproducible methods that minimize biases and errors. Systematic reviews serve different purposes and use a different methodology than other types of evidence synthesis such as narrative reviews, scoping reviews, and overviews of reviews. Systematic reviews can address questions regarding effects of interventions or exposures, diagnostic properties of tests, and prevalence or prognosis of diseases. All rigorous systematic reviews have common processes that include (1) determining the question and eligibility criteria, including a priori specification of subgroup hypotheses, (2) searching for evidence and selecting studies, (3) abstracting data and assessing risk of bias of the included studies, (4) summarizing the data for each outcome of interest, whenever possible using meta-analyses, and (5) assessing the certainty of the evidence and drawing conclusions. There are several tools that can guide and facilitate the systematic review process, but methodological and content expertise are always necessary.
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 | 0.132 | 0.287 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.037 | 0.039 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".