Issues related to the conduct of systematic reviews: a focus on the nutrition field
Bibliographic record
Abstract
Systematic reviews (SRs) are an increasingly popular evidence-based tool and are often used to answer complex research questions across many different research domains. Early SR methodology was advanced by social scientists, and the term meta-analysis was coined by a social scientist who also conducted research in psychology. SRs have recently become popular in healthcare and are likely to be beneficial in any field. The aim of this report is to highlight issues in SR conduct with a focus on the field of nutrition and to make recommendations on improving SR conduct in this area. Development of the research question is probably the most important step in conducting an SR. The 4 main components of an answerable question are 1) the patient, population, or problem; 2) the intervention, independent variable, or exposure; 3) the comparators; and 4) the dependent variables or outcomes of interest. The question will be used to determine the optimal methods for conducting the SR. SRs often include study designs beyond randomized trials and do not always include a meta-analysis of the results. Other topics explored include understanding and interpreting discordant reviews and the importance of reporting tools [eg, QUality Of Reporting Of Meta-analyses (QUOROM Statement) or CONsolidated Standards Of Reporting of Trials (CONSORT Statement)]. Recommendations are then provided, such as developing a capacity-building program, searching the primary literature for research gaps, and extending reporting tools such as the QUOROM Statement to the field of nutrition.
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 | 0.832 | 0.939 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.031 | 0.033 |
| Science and technology studies | 0.009 | 0.060 |
| Scholarly communication | 0.035 | 0.046 |
| Open science | 0.014 | 0.019 |
| Research integrity | 0.046 | 0.046 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".