Self-reported dietary energy intake of normal weight, overweight and obese adolescents
Bibliographic record
Abstract
OBJECTIVE: The purpose of the present paper was to assess dietary energy reporting as a function of sex and weight status among Ontario and Alberta adolescents, using the ratio of energy intake (EI) to estimated BMR (BMRest). DESIGN: Data were collected using the FBQ, a validated web-based dietary assessment tool (including a 24 h dietary recall, FFQ, and food and physical activity behavioural questions). BMI was calculated from self-reported height and weight and participants were classified as normal weight, overweight or obese. BMR was calculated using the WHO equations (based on weight). Reporting status was identified using the ratio EI:BMRest. SETTING: Data were collected in public, Catholic and private schools in Ontario and Alberta, Canada. SUBJECTS: A total of 1917 (n 876 male and n 1041 female) students (n 934 grade 9 and n 984 grade 10) participated. RESULTS: The mean EI:BMRest ratio across all participants was 1.4 (sd 0.6), providing evidence of under-reporting for the total sample. Females under-reported more than males (t = 6.27, P < 0.001), and under-reporting increased with increasing weight status for both males (F = 33.21, P < 0.001) and females (F = 14.28, P < 0.001). After removing those who reported eating less to lose weight, the EI:BMRest was 1.56 (sd 0.6) for males and 1.4 (sd 0.6) for females. CONCLUSION: The present study highlights methodological challenges associated with self-reported dietary data. Systematic differences in under-reporting of dietary intake by gender and weight status were observed using a web-based survey, similar to observations made using paper-based 24 h recalls and dietitian interviews.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".