Special Article Systematic review to support the development of nutrient reference intake values: challenges and solutions1–4
Bibliographic record
Abstract
Workshops sponsored by the Institute of Medicine (IOM) and the World Health Organization suggested that incorporating systematic (evidence) reviews into the process of updating nutrient reference values would provide a comprehensive and distilled evidence document to decision makers and enhance the transparency of the decision-making process the IOM used in recommending the Dietary Reference Intake values (DRIs) for US and Canadian populations. At the request of the US and Canadian government sponsors of the on-going review of the 1997 vitamin D and calcium DRI values, the Tufts Evidence-based Practice Center performed a systematic review for the current DRI Committee to use early in its deliberations. We described the approach used to include systematic review into the IOM process for updating nutrient reference values and highlighted major challenges encountered along with the solutions used. The challenges stemmed from the need to review and synthesize a large number of primary studies covering a broad range of outcomes. We resolved these challenges by 1) working with a technical expert panel to prioritize and select outcomes of interest, 2) developing methods to use existing systematic reviews and documenting the lim-itations by doing so, 3) translating results from studies not designed to address issues of interest by using a transparent process, and 4) estab-lishing tailored quality-assessment tools to assist in decision making. The experiences described in this article can serve as a basis for future improvements in systematic reviews of nutrients and to better integrate systematic review into development of future nutrient reference val-ues. Am J Clin Nutr doi: 10.3945/ajcn.2009.29092.
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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 | 0.268 | 0.651 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.022 | 0.016 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".