Comparison of response rates, intake estimates, and preferences between the National Cancer Institute’s Automated Self‐Administered 24‐Hour Recall and interviewer‐administered Automated Multiple Pass Method recalls (36.7)
Bibliographic record
Abstract
Objective: To compare response rates, intake estimates, and preferences between ASA24 and interviewer‐administered AMPM recalls. Methods: About 1200 participants were recruited from three integrated health systems using quota sampling to ensure representation of a range of ages and race/ethnicity groups. Participants were asked to complete two 24HRs, 4‐7 weeks apart, and randomized into four study groups: 1) two ASA24s; 2) two AMPMs; 3) ASA24 first and AMPM second; and 4) AMPM first and ASA24 second. Results: Almost all enrolled participants (95%) completed at least one recall and 80% completed two; response rates did not differ by recall mode. Estimated intakes of energy, nutrients and food groups were comparable for ASA24 and AMPM; for example, energy, 2132 vs. 2126 kcal; fat, 84.9 vs. 82.8 g; saturated fat, 27.9 vs. 26.9 g; fiber, 18.4 vs. 18.4 g; and fruits and vegetables, 3.0 vs. 3.1 cup equivalents. Of participants completing one ASA24 and one AMPM, a greater percentage overall and by sex and site preferred ASA24. Discussion: These findings show that ASA24 performs well relative to AMPM recalls. ASA24 offers significant savings over interviewer‐administered recalls, is publicly available from NCI at no charge, and has been used in over 800 studies to collect over 113,000 recalls, indicating its feasibility for use in large‐scale research. ASA24 is currently being updated to run on mobile applications.
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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 | 0.016 | 0.027 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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; 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".