Conducting systematic reviews of association (etiology)
Bibliographic record
Abstract
The systematic review of evidence is the research method which underpins the traditional approach to evidence-based healthcare. There is currently no uniform methodology for conducting a systematic review of association (etiology). This study outlines and describes the Joanna Briggs Institute's approach and guidance for synthesizing evidence related to association with a predominant focus on etiology and contributes to the emerging field of systematic review methodologies. It should be noted that questions of association typically address etiological or prognostic issues.The systematic review of studies to answer questions of etiology follows the same basic principles of systematic review of other types of data. An a priori protocol must inform the conduct of the systematic review, comprehensive searching must be performed and critical appraisal of retrieved studies must be carried out.The overarching objective of systematic reviews of etiology is to identify and synthesize the best available evidence on the factors of interest that are associated with a particular disease or outcome. The traditional PICO (population, interventions, comparators and outcomes) format for systematic reviews of effects does not align with questions relating to etiology. A systematic review of etiology should include the following aspects: population, exposure of interest (independent variable) and outcome (dependent variable).Studies of etiology are predominantly explanatory or predictive. The objective of reviews of explanatory or predictive studies is to contribute to, and improve our understanding of, the relationship of health-related events or outcomes by examining the association between variables. When interpreting possible associations between variables based on observational study data, caution must be exercised due to the likely presence of confounding variables or moderators that may impact on the results.As with all systematic reviews, there are various approaches to present the results, including a narrative, graphical or tabular summary, or meta-analysis. When meta-analysis is not possible, a set of alternative methods for synthesizing research is available. On the basis of the research question and objectives, narrative, tabular and/or visual approaches can be used for data synthesis. There are some special considerations when conducting meta-analysis for questions related to risk and correlation. These include, but are not limited to, causal inference.Systematic review and meta-analysis of studies related to etiology is an emerging methodology in the field of evidence synthesis. These reviews can provide useful information for healthcare professionals and policymakers on the burden of disease. The standardized Joanna Briggs Institute approach offers a rigorous and transparent method to conduct reviews of etiology.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
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 | 0.449 | 0.709 |
| Meta-epidemiology (narrow) | 0.008 | 0.009 |
| Meta-epidemiology (broad) | 0.029 | 0.032 |
| Bibliometrics | 0.057 | 0.040 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.049 | 0.011 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".