Accuracy of portion size reporting in the Automated Self‐Administered 24‐hour recall (ASA24) compared to interviewer‐administered recalls (36.6)
Bibliographic record
Abstract
Objective: To assess accuracy of portion size reporting in ASA24 compared to interviewer‐administered recalls. Methods: True intake for three meals was ascertained among 81 adults by inconspicuously weighing foods and beverages offered at a buffet before and after each participant served him/herself, as well as plate waste. Participants were randomly assigned to complete ASA24 or an interviewer‐administered Automated Multiple‐Pass Method (AMPM) recall the following day. Differences between true and reported gram weights were calculated for each consumed item for which a match was reported by respondents and averaged. Results: For foods and drinks truly consumed for which respondents reported a match (80% for ASA24 and 83% for AMPM), mean differences between true and reported portion sizes were ‐3.7 grams for ASA24 and ‐11.8 grams for AMPM (absolute differences: 40.5 grams for ASA24 and 39.8 grams for AMPM (p=0.44)). Differences for primary foods and drinks were ‐5.1 grams for ASA24 and ‐14.4 grams for AMPM, and for additions to or ingredients in multi‐component items, 0.60 grams for ASA24 and ‐6.2 grams for AMPM. Discussion: Previous analyses indicate similar match rates (i.e., the proportion of items consumed for which a corresponding food or drink was reported) and energy, nutrient and food group estimates between ASA24 and AMPM. The current results suggest that the accuracy of portion size reporting is also similar, providing further evidence that ASA24 performs well relative to interviewer‐administered recalls.
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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 | 0.009 | 0.033 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".