Information retrieval for systematic reviews in food and feed topics: A narrative review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Systematic review methods are now being used for reviews of food production, food safety and security, plant health, and animal health and welfare. Information retrieval methods in this context have been informed by human health-care approaches and ideally should be based on relevant research and experience. OBJECTIVE: This narrative review seeks to identify and summarize current research-based evidence and experience on information retrieval for systematic reviews in food and feed topics. METHODS: MEDLINE (Ovid), Science Citation Index (Web of Science), and ScienceDirect (http://www.sciencedirect.com/) were searched in 2012 and 2016. We also contacted topic experts and undertook citation searches. We selected and summarized studies reporting research on information retrieval, as well as published guidance and experience. RESULTS: There is little published evidence on the most efficient way to conduct searches for food and feed topics. There are few available study design search filters, and their use may be problematic given poor or inconsistent reporting of study methods. Food and feed research makes use of a wide range of study designs so it might be best to focus strategy development on capturing study populations, although this also has challenges. There is limited guidance on which resources should be searched and whether publication bias in disciplines relevant to food and feed necessitates extensive searching of the gray literature. CONCLUSIONS: There is some limited evidence on information retrieval approaches, but more research is required to inform effective and efficient approaches to searching to populate food and feed reviews.
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.
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 | 0.778 | 0.851 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.033 | 0.006 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it