Influence of editing free text responses on nutrient and food group estimates from the Automated Self‐Administered 24‐hour dietary recall (ASA24) (1022.7)
Bibliographic record
Abstract
OBJECTIVE : To assess the impact of editing data from the ASA24 on nutrient and food group estimates. METHODS : About 900 adults from 3 integrated health care systems completed recalls in an ASA24 evaluation study. In using ASA24, respondents may enter free text in 2 contexts. “Other” is a response to questions about food detail (e.g., brand name), in which case text is collected but not used in assigning food codes. Text can be entered for “Unfound foods” when respondents cannot find a food. The system asks questions to identify the food type and assigns a food code. Free‐text responses were compared to system‐assigned food codes and edited when necessary. Nutrient and food group values for unedited and edited data were then compared. RESULTS : Of 1,013 recalls collected, 440 recalls included 749 foods with free text responses. The ASA24 system assigned correct food codes 70% of the time. Edits were made to 225 foods (30% of foods reported using free text), affecting 188 recalls (18.5% of all recalls). This editing process required over 60 hours of specialized staff time. The mean nutrient and food group values before and after editing showed no significant differences. DISCUSSION : These results allow researchers using ASA24 to weigh the benefits and costs of manually reviewing free text responses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.077 | 0.494 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".