Comparison of dietary supplement intake reporting between the Automated Self‐Administered 24‐Hour Recall (ASA24) and Automated Multiple Pass Method (AMPM) recalls (245.2)
Bibliographic record
Abstract
Objective: To compare dietary supplement use reported on self‐administered (ASA24) versus interviewer‐administered (AMPM) 24‐hour recalls. Methods: About 1200 women and men were recruited from three integrated health systems using quota sampling to capture a range of ages and racial/ethnic backgrounds. Participants were randomized into four groups and asked to complete two recalls: 1) two ASA24s; 2) two AMPMs; 3) ASA24 first, AMPM second; 4) AMPM first, ASA24 second. Dietary supplements were coded using the 2007‐2008 NHANES Dietary Supplement Database. Difference in prevalence of reported supplement use was tested using a Chi‐square test. Results: Food and supplement data were complete for 1089 participants. Prevalence of use of any dietary supplement on either day was not significantly different by recall mode overall (47.8% for AMPM (n=1022) vs.48.5% for ASA24 (n=1012); p=0.82) or within sex, age, or race/ethnicity groups. Among supplement users, the prevalence of reported multivitamin use was significantly higher on AMPM compared to ASA24 (65.9% vs. 57.2%; p <0.01). In ASA24, 50 participants reported 114 supplements as “unknown”, meaning they were unable to find their supplement in the ASA24 database. Discussion: The ASA24 performs similarly to AMPM for estimating overall prevalence of dietary supplement use. Significant differences found in the reporting of multivitamins specifically could be related to the number of unknown supplements reported in ASA24. Coding of text entered for supplements not found in ASA24 may be necessary to obtain accurate prevalence of supplement type and nutrient estimates. Based on these findings, future versions of ASA24 will include questions when unknown supplements are reported for the purpose of assigning reasonable default supplement codes.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".