The impact of the peer review of literature search strategies in support of rapid review reports
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to investigate the impact of the peer review of literature search strategies prepared in support of rapid reviews. METHODS: A sample of 200 CADTH rapid reviews was selected. For each rapid review meeting the inclusion criteria, the pre-peer-reviewed and corresponding post-peer-reviewed search strategies were run, and the search results were compared. Bibliographic records retrieved solely by the post-peer-reviewed search strategy and included in the rapid review report were identified as representing "included studies." The publication type of each included study was determined, and the attributes of the corresponding record were analyzed to determine the reason for its retrieval by the post-peer-reviewed search. RESULTS: The peer review of search strategies resulted in the retrieval of one or more additional records for 75% of the searches investigated, but only a small proportion of these records (4%) represented included studies. The main publication types of the included studies were nonrandomized studies (60%) and narrative reviews (20%). The principal changes to search strategies that resulted in the retrieval of additional included studies were the inclusion of more keywords or subject headings or a change in the way concepts were combined. CONCLUSIONS: The peer review of literature search strategies aids in the retrieval of relevant records particularly those representing nonrandomized studies. The scrutiny of keywords, subject headings, and the relation between search concepts are key components of the peer review process.
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 | 0.754 | 0.952 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.030 | 0.028 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.026 | 0.020 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".