Systematic reviews and meta-analyses [State of the art series. Operational research. Number 5 in the series]
Bibliographic record
Abstract
Properly performed systematic reviews can furnish an accurate summary of the published literature and thereby provide essential information for clinicians, guideline panels and policy makers. These reviews are increasingly used to develop management guidelines, and are now required by many agencies before considering funding for new research. A successful and rigorous review starts with the development of a clear question structured using the PICO format (population, intervention, comparison and outcomes). Prior to initiating the review, a protocol should be written that outlines study selection criteria, based on the same PICO format and study design. The search, also based on the PICO format, should be performed with the assistance of someone experienced in searches and involve multiple electronic databases without language restrictions. Additional sources include hand searches, the grey literature and correspondence with authors. Once a comprehensive list of titles has been made, selection of studies should be made by two reviewers working independently: first based on the titles, then the abstracts, then full text. Analysis is initially descriptive, including assessment of quality using published and validated checklists. Summary estimates from pooling (i.e., formal meta-analysis) are appropriate if the methodological differences between studies are not clinically important and if quantitative estimates of heterogeneity are acceptable. Reporting should follow published guidelines. Reviews that follow these methods can have a substantial impact on practice and policy. These reviews can help identify research priorities as areas where knowledge is uncertain, and also those topics where little additional work is required.
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 | 0.101 | 0.330 |
| Meta-epidemiology (narrow) | 0.009 | 0.006 |
| Meta-epidemiology (broad) | 0.021 | 0.013 |
| Bibliometrics | 0.061 | 0.056 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.111 | 0.075 |
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".