PRESS Peer Review of Electronic Search Strategies: 2015 Guideline Statement
Bibliographic record
Abstract
OBJECTIVE: To develop an evidence-based guideline for Peer Review of Electronic Search Strategies (PRESS) for systematic reviews (SRs), health technology assessments, and other evidence syntheses. STUDY DESIGN AND SETTING: An SR, Web-based survey of experts, and consensus development forum were undertaken to identify checklists that evaluated or validated electronic literature search strategies and to determine which of their elements related to search quality or errors. RESULTS: Systematic review: No new search elements were identified for addition to the existing (2008-2010) PRESS 2015 Evidence-Based Checklist, and there was no evidence refuting any of its elements. Results suggested that structured PRESS could identify search errors and improve the selection of search terms. Web-based survey of experts: Most respondents felt that peer review should be undertaken after the MEDLINE search had been prepared but before it had been translated to other databases. Consensus development forum: Of the seven original PRESS elements, six were retained: translation of the research question; Boolean and proximity operators; subject headings; text word search; spelling, syntax and line numbers; and limits and filters. The seventh (skilled translation of the search strategy to additional databases) was removed, as there was consensus that this should be left to the discretion of searchers. An updated PRESS 2015 Guideline Statement was developed, which includes the following four documents: PRESS 2015 Evidence-Based Checklist, PRESS 2015 Recommendations for Librarian Practice, PRESS 2015 Implementation Strategies, and PRESS 2015 Guideline Assessment Form. CONCLUSION: The PRESS 2015 Guideline Statement should help to guide and improve the peer review of electronic literature search strategies.
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 | 0.250 | 0.544 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.012 | 0.012 |
| Research integrity | 0.020 | 0.015 |
| Insufficient payload (model declined to judge) | 0.017 | 0.021 |
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".