Use of the Automated Self‐administered 24‐hour Recall (ASA24) in the Real World
Bibliographic record
Abstract
Objective To describe the use of NCI's ASA24 in real‐world studies. Methods Descriptive statistics were generated to summarize usage characteristics across studies using 2014 versions of ASA24. Results Since Feb. 2014, 319 studies had registered to use ASA24, 10% of which requested ASA24‐Kids. The percentages of studies using the following setup options were: 88% ‐ allowed unscheduled logins; 60% ‐ completion of recalls from midnight‐to‐midnight vs. past 24‐hrs; 73% ‐ allowed multiple vs. single logins; 68% ‐ completion time restricted to 24 vs. 32 hrs. The percentages of studies including optional questions were: 49% ‐ dietary supplements; 82% ‐ location of meals; 46% ‐whom meals were eaten with; 45% ‐ use of TV/computer/mobile phone during meals; 35% ‐ source of foods consumed. Nearly all recalls were completed after a single login; mean times to complete recalls with and without supplement questions was 32 and 23 minutes, respectively. Mean number of foods and beverages reported/day was 13. Mean and median number of participants/study was 509 and 72, respectively (range: 1‐100,000); mean and median number of recalls per participant was 8 and 3, respectively (range 1‐335). Most researchers (90%) were affiliated with academic institutions or government agencies. About 37% of studies had research funding; 14% were NIH‐funded. Two‐thirds of studies are using ASA24 for epidemiologic, intervention or clinical research. Conclusion ASA24 is being used by a diversity of researchers in a variety of ways to meet their research and teaching needs. Funding : NCI.
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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 | 0.039 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".