Meta‐analysis of prevalence: <scp><i>I</i><sup>2</sup></scp> statistic and how to deal with heterogeneity
Bibliographic record
Abstract
Abstract Over the last decade, there has been a 10‐fold increase in the number of published systematic reviews of prevalence. In meta‐analyses of prevalence, the summary estimate represents an average prevalence from included studies. This estimate is truly informative only if there is no substantial heterogeneity among the different contexts being pooled. In systematic reviews, heterogeneity is usually explored with I ‐squared statistic ( I 2 ), but this statistic does not directly inform us about the distribution of effects and frequently systematic reviewers and readers misinterpret this result. In a sample of 134 meta‐analyses of prevalence, the median I 2 was 96.9% (IQR 90.5–98.7). We observed larger I 2 in meta‐analysis with higher number of studies and extreme pooled estimates (defined as <10% or >90%). Studies with high I 2 values were more likely to have conducted a sensitivity analysis, including subgroup analysis but only three (2%) systematic reviews reported prediction intervals. We observed that meta‐analyses of prevalence often present high I 2 values. However, the number of studies included in the meta‐analysis and the point estimate can be associated with the I 2 value, and a high I 2 value is not always synonymous with high heterogeneity. In meta‐analyses of prevalence, I 2 statistics may not be discriminative and should be interpreted with caution, avoiding arbitrary thresholds. To discuss heterogeneity, reviewers should focus on the description of the expected range of estimates, which can be done using prediction intervals and planned sensitivity analysis.
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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 | Metaresearch Domain: Methods · 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 | Meta-analysis | 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 | 0.133 | 0.488 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.018 | 0.025 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.033 | 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, 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".