Reliability and reproducibility of systematic reviews informing the 2020–2025 Dietary Guidelines for Americans: a pilot study
Bibliographic record
Abstract
BACKGROUND: Although high-quality nutrition systematic reviews (SRs) are important for clinical decision making, there remains debate on their methodological quality and reporting transparency. OBJECTIVES: The objective of this study was to assess the reliability and reproducibility of a sample of SRs produced by the Nutrition Evidence Systematic Review (NESR) team to inform the 2020-2025 Dietary Guidelines for Americans (DGAs). METHODS: We evaluated a sample of 8 SRs from the DGA dietary patterns subcommittee for methodological quality using the Assessment of Multiple Systematic Reviews 2 (AMSTAR 2) tool and for reporting transparency using the PRISMA 2020 and PRISMA literature search extension (PRISMA-S) checklists. We assessed the quality and reproducibility of the original search strategy of one selected SR using the Peer Review of Electronic Search Strategies checklist. The reporting transparency of the SR's narrative data synthesis was assessed using the Synthesis Without Meta-Analysis (SWiM) checklist. Interpretation bias was evaluated using existing spin bias classifications in systematic reviews. RESULTS: The AMSTAR 2 assessment identified critical methodological weaknesses, and all included SRs were judged to be of critically low quality. Overall, 74% of the PRISMA 2020 checklist items and 63% of the PRISMA-S checklist items were satisfactorily fulfilled. We identified several errors and inconsistencies in the search strategy and could not reproduce searches within a 10% margin of the original results. The SWiM assessment identified concerns regarding the reporting transparency of the narrative data synthesis, but the spin bias assessment revealed no evidence of interpretation bias. CONCLUSIONS: Several methodological quality and reporting concerns were identified, which could lead to reliability and reproducibility issues should a full reproduction attempt be made. However, additional research is needed to confirm the impact of these findings on conclusions statements and their generalizability across the NESR team SRs. This study was registered in the Open Science Framework (https://osf.io/ns6a9/).
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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 | 0.693 | 0.884 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.015 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.003 |
| 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, 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".