Assessments of risk of bias in systematic reviews of observational nutritional epidemiologic studies are often not appropriate or comprehensive: a methodological study
Bibliographic record
Abstract
BACKGROUND: An essential component of systematic reviews is the assessment of risk of bias. To date, there has been no investigation of how reviews of non-randomised studies of nutritional exposures (called 'nutritional epidemiologic studies') assess risk of bias. OBJECTIVE: To describe methods for the assessment of risk of bias in reviews of nutritional epidemiologic studies. METHODS: We searched MEDLINE, EMBASE and the Cochrane Database of Systematic Reviews (Jan 2018-Aug 2019) and sampled 150 systematic reviews of nutritional epidemiologic studies. RESULTS: Most reviews (n=131/150; 87.3%) attempted to assess risk of bias. Commonly used tools neglected to address all important sources of bias, such as selective reporting (n=25/28; 89.3%), and frequently included constructs unrelated to risk of bias, such as reporting (n=14/28; 50.0%). Most reviews (n=66/101; 65.3%) did not incorporate risk of bias in the synthesis. While more than half of reviews considered biases due to confounding and misclassification of the exposure in their interpretation of findings, other biases, such as selective reporting, were rarely considered (n=1/150; 0.7%). CONCLUSION: Reviews of nutritional epidemiologic studies have important limitations in their assessment of risk of bias.
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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 | 0.682 | 0.891 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.018 | 0.020 |
| Bibliometrics | 0.029 | 0.032 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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, 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".