Review Typology: The Basic Types of Reviews for Synthesizing Evidence for the Purpose of Knowledge Translation.
Bibliographic record
Abstract
With advances in medical practice and fields of research, reviews occupy a key position for summarizing existing knowledge. Due to the differences and overlap in terminology, the full potential for reviews may be lost due to confusion of indistinct approaches. The main objective of this study was to provide a descriptive outline of each of the common review types with their characteristics and examples in a health care system. Ascoping search was conducted using the keywords associated with the literature review typology. The SALSA(Search, Appraisal, Synthesis and Analysis) analytical framework was used to identify and distinguish each type of review. Nine common types of reviews and associated methodologies were evaluated against the already established SALSA framework. Their description, strengths and weaknesses are presented. The results provided a basic idea of different types of reviews based on the intended level of knowledge synthesis by which researchers can identify the appropriate type of review based on their intended audience.
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 | 0.086 | 0.316 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.057 | 0.059 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".