Combining nutrient intake from food/beverages and vitamin/mineral supplements.
Bibliographic record
Abstract
BACKGROUND: To calculate total intake of a nutrient and estimate inadequate intake for a population, the amounts derived from food/beverages and from vitamin/mineral supplements must be combined. The two methods Statistics Canada has suggested present problems of interpretation. DATA AND METHODS: Data collected from 34,386 respondents to the 2004 Canadian Community Health Survey-Nutrition were used to compare four methods of combining nutrient intake from food/beverages and vitamin/mineral supplements: adding average intake from supplements to the 24-hour food/beverage recall and estimating the usual distribution in the population (Method 1); estimating usual individual intake from food? beverages and adding intake from supplements (Method 2); and dividing the population into supplement users and non-users and applying Method 1 or Method 2 and combining the estimates based on the percentages of users and non-users (Methods 3 and 4). RESULTS: Interpretation problems arise with Methods 1 and 2; for example, the percentage of the population with inadequate intake of vitamin C and folate equivalents falls outside the expected minimum-maximum range. These interpretation problems are not observed with Methods 3 and 4. INTERPRETATION: Interpretation problems that may arise in combining food and supplement intake of a given nutrient are overcome if the population is divided into supplement users and non-users before Method 1 or Method 2 is applied.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".