Leveraging questionnaire-based physical activity levels (PAL) to identify energy intake misreporting using the Goldberg method: A doubly labeled water validation study
Bibliographic record
Abstract
ABSTRACT The Goldberg method has been suggested for identifying energy intake (EI) under-reporting in nutritional epidemiology. Its implementation, however, is limited by challenges associated with estimating physical activity levels (PAL). We quantified the accuracy of the Sedentary Time and Activity Reporting Questionnaire (STAR-Q) derived PAL (PAL STAR-Q ) combined with the Goldberg method (Goldberg-PAL STARQ ) to identify EI misreporting as compared with doubly labeled water (DLW) derived total energy expenditure (TEE DLW ). Between 2009 and 2011, 99 men and women completed a two-week DLW protocol, a food frequency questionnaire, and the STAR-Q. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of the Goldberg-PAL STAR-Q were determined. Fifty-eight percent of men and women were classified as under-reporters by Goldberg-PAL STAR-Q compared with 60% of men and 56% of women by TEE DLW . Among men, sensitivity, specificity, PPV, NPV and accuracy and 95% confidence intervals were 88% (68%-97%), 87% (61%-98%), 91% (72%-99%), 81% (56%-94%), and 87% (72%-95%), respectively; and among women 79% (62%-90%), 69% (50%-84%), 77% (60%-88%), 72% (52%-86%), and 75% (62%-84%), respectively. Validated individual level PALs used with the Goldberg method can be informative in sensitivity analyses to gain insight into EI misreporting in nutritional epidemiology studies lacking in objective EI measures.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".