Lack of transparency in reporting narrative synthesis of quantitative data: a methodological assessment of systematic reviews
Bibliographic record
Abstract
OBJECTIVE: To assess the adequacy of reporting and conduct of narrative synthesis of quantitative data (NS) in reviews evaluating the effectiveness of public health interventions. STUDY DESIGN AND SETTING: A retrospective comparison of a 20% (n = 474/2,372) random sample of public health systematic reviews from the McMaster Health Evidence database (January 2010-October 2015) to establish the proportion of reviews using NS. From those reviews using NS, 30% (n = 75/251) were randomly selected and data were extracted for detailed assessment of: reporting NS methods, management and investigation of heterogeneity, transparency of data presentation, and assessment of robustness of the synthesis. RESULTS: Most reviews used NS (56%, n = 251/446); meta-analysis was the primary method of synthesis for 44%. In the detailed assessment of NS, 95% (n = 71/75) did not describe NS methods; 43% (n = 32) did not provide transparent links between the synthesis data and the synthesis reported in the text; of 14 reviews that identified heterogeneity in direction of effect, only one investigated the heterogeneity; and 36% (n = 27) did not reflect on limitations of the synthesis. CONCLUSION: NS methods are rarely reported in systematic reviews of public health interventions and many NS reviews lack transparency in how the data are presented and the conclusions are reached. This threatens the validity of much of the evidence synthesis used to support public health. Improved guidance on reporting and conduct of NS will contribute to improved utility of NS systematic reviews.
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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: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | MetaresearchMeta-epidemiology (broad) Domain: Reporting · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Systematic review | 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 | 0.871 | 0.967 |
| Meta-epidemiology (narrow) | 0.004 | 0.008 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.027 | 0.025 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.022 | 0.024 |
| Open science | 0.009 | 0.018 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".