Systematic reviews on selected nutrition interventions: descriptive assessment of conduct and methodological challenges
Bibliographic record
Abstract
Rigorous and transparent systematic reviews are recognized internationally as a credible source for evidence of effectiveness. However, in the field of nutrition, despite attempts at developing consensus on actions and interventions to reduce undernutrition and micronutrient deficiencies, there is lack of coordination among various groups. The aim of this overview of systematic review is to assess the process and conduct of systematic reviews published in the past 5 years to make recommendations on improving process and methodology of systematic reviews in the field of nutrition. We identified nine interventions from four areas of nutrition through a consultative process and conducted a comprehensive search to identify systematic reviews on the selected interventions published in the last 5 years. We identified 90 systematic reviews across these nine intervention areas. The median overall Assessment of Multiple Systematic Reviews (AMSTAR) score was 8 (range 2–11) with methodological quality of Cochrane reviews being fairly consistent with a median AMSTAR score of 10 (range 8–11), while for the non-Cochrane reviews, it ranged from 2 to 11 with a median of 7.5. From the 11-point AMSTAR criteria, 91% of the reviews followed an a priori design, 81% did duplicate screening and data extraction, 88% conducted a comprehensive search, 64% used status of publication as an inclusion criteria, 44% provided the list of included and excluded studies, 60% assessed and documented the scientific quality of the included studies and used it in interpreting the results, 61% used appropriate methods to combine the results, 40% assessed publication bias and 82% stated the conflict of interest. We found considerable variation in methodological quality, lack of standardization of outcomes, lack of standardized assessment of risk of bias of included studies, variation in study designs included and variation in how heterogeneity was handled. Each of these methodological choices influences the findings of the reviews, and lack of standardization across these domains increases the complexity for users of systematic reviews in interpreting results. There is a need to develop a consensus on methodologies for nutrition reviews, criteria for assessing the evidence and possibly facilitating development and collation of the evidence in the subject area.
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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 | 0.708 | 0.882 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.045 | 0.049 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".