The National Cancer Institute's Dietary Assessment Primer
Bibliographic record
Abstract
The efficacy of all dietary studies, whether aimed at monitoring a population's diet, understanding diet and health relationships, or evaluating the effect of an intervention, depends on appropriate assessment of intakes. Determining the most suitable dietary instrument for a particular study can be a challenge and is dependent upon a number of considerations, including the specific research question, dietary components of interest, study design, and target population. The National Cancer Institute has developed an online resource to provide guidance on the selection of appropriate dietary assessment methodologies for characterizing the intakes of a group or groups and to outline various considerations for the use of each. The Dietary Assessment Primer describes the major types of self‐report instruments; provides guidance on using the instruments alone or in combination to address different research questions; compares key features of each instrument; explains validity, measurement error, and calibration in the context of dietary assessment; provides expanded information about particular key topics; and includes a Glossary of basic terms and an extensive list of References and Resources. The online tool was developed by subject matter experts within NCI, with internal and external review to ensure the content reflects the current state of the science. User‐centered design principles were applied to developing the web‐based interface to ensure optimal usability. The Primer is intended to help researchers collect the highest quality dietary data possible given their study resources and constraints, with the ultimate goal of improving our capacity to monitor diets among populations and understand how diet affects health.
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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 | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.066 | 0.039 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".