The interpretation of systematic reviews with meta-analyses: an objective or subjective process?
Bibliographic record
Abstract
BACKGROUND: Discrepancies between the conclusions of different meta-analyses (quantitative syntheses of systematic reviews) are often ascribed to methodological differences. The objective of this study was to determine the discordance in interpretations when meta-analysts are presented with identical data. METHODS: We searched the literature for all randomized clinical trials (RCT) and review articles on the efficacy of intravenous magnesium in the early post-myocardial infarction period. We organized the articles chronologically and grouped them in packages. The first package included the first RCT, and a summary of the review articles published prior to first RCT. The second package contained the second and third RCT, a meta-analysis based on the data, and a summary of all review articles published prior to the third RCT. Similar packages were created for the 5th RCT, 10th RCT, 20th RCT and 23rd RCT (all articles). We presented the packages one at a time to eight different reviewers and asked them to answer three clinical questions after each package based solely on the information provided. The clinical questions included whether 1) they believed magnesium is now proven beneficial, 2) they believed magnesium will eventually be proven to be beneficial, and 3) they would recommend its use at this time. RESULTS: There was considerable disagreement among the reviewers for each package, and for each question. The discrepancies increased when the heterogeneity of the data increased. In addition, some reviewers became more sceptical of the effectiveness of magnesium over time, and some reviewers became less sceptical. CONCLUSION: The interpretation of the results of systematic reviews with meta-analyses includes a subjective component that can lead to discordant conclusions that are independent of the methodology used to obtain or analyse the data.
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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 | 0.774 | 0.923 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.021 | 0.012 |
| Bibliometrics | 0.035 | 0.032 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.029 | 0.024 |
| Open science | 0.013 | 0.013 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 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".