Rapid reviews versus full systematic reviews: An inventory of current methods and practice in health technology assessment
Bibliographic record
Abstract
OBJECTIVES: This review assessed current practice in the preparation of rapid reviews by health technology assessment (HTA) organizations, both internationally and in the Australian context, and evaluated the available peer-reviewed literature pertaining to the methodology used in the preparation of these reviews. METHODS: A survey tool was developed and distributed to a total of fifty International Network of Agencies for Health Technology Assessment (INAHTA) members and other selected HTA organizations. Data on a broad range of themes related to the conduct of rapid reviews were collated, discussed narratively, and subjected to simple statistical analysis where appropriate. Systematic searches of the Cochrane Library, EMBASE, MEDLINE, and the Australian Medical Index were undertaken in March 2007 to identify literature pertaining to rapid review methodology. Comparative studies, guidelines, program evaluations, methods studies, commentaries, and surveys were considered for inclusion. RESULTS: Twenty-three surveys were returned (46 percent), with eighteen agencies reporting on thirty-six rapid review products. Axiomatic trends were identified, but there was little cohesion between organizations regarding the contents, methods, and definition of a rapid review. The twelve studies identified by the systematic literature search did not specifically address the methodology underpinning rapid review; rather, many highlighted the complexity of the area. Authors suggested restricted research questions and truncated search strategies as methods to limit the time taken to complete a review. CONCLUSIONS: Rather than developing a formalized methodology by which to conduct rapid reviews, agencies should work toward increasing the transparency of the methods used for each review. It is perhaps the appropriate use, not the appropriate methodology, of a rapid review that requires future consideration.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.063 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.007 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads 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".