Are systematic reviews addressing nutrition for cancer prevention trustworthy? A systematic survey of quality and risk of bias
Bibliographic record
Abstract
CONTEXT: The last 30 years have yielded a vast number of systematic reviews and/or meta-analyses addressing the link between nutrition and cancer risk. OBJECTIVE: The aim of this survey was to assess overall quality and potential for risk of bias in systematic reviews and meta-analyses (SRMAs) that examined the role of nutrition in cancer prevention. DATA SOURCES: MEDLINE, Embase, and the Cochrane Library databases were searched (last search performed November 2018). STUDY SELECTION: Studies identified as SRMAs that investigated a nutritional or dietary intervention or exposure for cancer prevention in the general population or in people at risk of cancer and in which primary studies had a comparison group were eligible for inclusion. Screening, data extraction, and quality assessment were conducted independently by 2 reviewers. DATA EXTRACTION: Altogether, 101 studies were randomly selected for analysis. The methodological quality and risk of bias were evaluated using the AMSTAR-2 and ROBIS tools, respectively. RESULTS: Most SRMAs included observational studies. Less than 10% of SRMAs reported a study protocol, and only 51% of SRMAs assessed the risk of bias in primary studies. Most studies conducted subgroup analyses, but only a few reported tests of interaction or specified subgroups of interest a priori. Overall, according to AMSTAR-2, only 1% of SRMAs were of high quality, while 97% were of critically low quality. Only 3% had a low risk of bias, according to ROBIS. CONCLUSIONS: This systematic survey revealed substantial limitations with respect to quality and risk of bias of SRMAs. SRMAs examining nutrition and cancer prevention cannot be considered trustworthy, and results should be interpreted with caution. Peer reviewers as well as users of SRMAs should be advised to use the AMSTAR-2 and/or ROBIS instruments to help to determine the overall quality and risk of bias of SRMAs. SYSTEMATIC REVIEW REGISTRATION: PROSPERO registration number CRD42019121116.
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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: Evaluation · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | MetaresearchMeta-epidemiology (broad) Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | medium |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".