Compliance of systematic reviews in veterinary journals with Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) literature search reporting guidelines
Bibliographic record
Abstract
OBJECTIVE: Complete, accurate reporting of systematic reviews facilitates assessment of how well reviews have been conducted. The primary objective of this study was to examine compliance of systematic reviews in veterinary journals with Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines for literature search reporting and to examine the completeness, bias, and reproducibility of the searches in these reviews from what was reported. The second objective was to examine reporting of the credentials and contributions of those involved in the search process. METHODS: A sample of systematic reviews or meta-analyses published in veterinary journals between 2011 and 2015 was obtained by searching PubMed. Reporting in the full text of each review was checked against certain PRISMA checklist items. RESULTS: Over one-third of reviews (37%) did not search the CAB Abstracts database, and 9% of reviews searched only 1 database. Over two-thirds of reviews (65%) did not report any search for grey literature or stated that they excluded grey literature. The majority of reviews (95%) did not report a reproducible search strategy. CONCLUSIONS: Most reviews had significant deficiencies in reporting the search process that raise questions about how these searches were conducted and ultimately cast serious doubts on the validity and reliability of reviews based on a potentially biased and incomplete body of literature. These deficiencies also highlight the need for veterinary journal editors and publishers to be more rigorous in requiring adherence to PRISMA guidelines and to encourage veterinary researchers to include librarians or information specialists on systematic review teams to improve the quality and reporting of searches.
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 | 0.742 | 0.839 |
| Meta-epidemiology (narrow) | 0.004 | 0.009 |
| Meta-epidemiology (broad) | 0.016 | 0.022 |
| Bibliometrics | 0.043 | 0.046 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.010 | 0.011 |
| Research integrity | 0.013 | 0.011 |
| 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; 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".