Skin Carotenoid Score Level and Race Affect Intradevice Repeatability of Veggie Meter® at a Single Time Point
Bibliographic record
Abstract
OBJECTIVE: To compare the reliability of 2 methods (3 scan average vs average of scan 2 and 3) of skin carotenoid score (SCS) measurement and identify participant-level factors affecting intradevice repeatability of Veggie Meter® (VM). DESIGN: Cross-sectional study in Illinois. PARTICIPANTS: The sample (N = 587) included about 36% children, 27% adolescents, and 32% adults; 67% were White, 14% Asian, 10% Black or African American, 8% mixed race, 1% Native Hawaiian, and 0.4% American Indian; 92% were non-Hispanic, and 53% female. VARIABLE MEASURED: Three consecutive SCS readings using 2 devices. Self-reported or parent-reported demographic information. ANALYSIS: Paired sample t-tests compared the coefficient of variation of the 3-scan average SCS with the coefficient of variation of the second and third-scan average SCS. We conducted sign tests and generated Bland-Altman plots to determine the type of errors. Kruskal-Wallis and Mann-Whitney tests assessed if repeatability coefficients of the 3 consecutive SCS readings were different across age, race, ethnicity, and SCS quartiles, where repeatability coefficients = within-subject SD × √2 × 1.96. RESULTS: = 9.7, P = 0.02). Pairwise comparisons showed that repeatability coefficients were significantly lower for Black or African American participants than White (mean SCS difference = 22.3, P = 0.001) and Asian participants (mean SCS difference = 23.7, P = 0.005) in one device (VM 1). CONCLUSION AND IMPLICATIONS: Findings support using the average of the second and third scans as a more reliable method for assessing SCS. Reporting device-specific repeatability coefficients for the VM may enhance the interpretability of SCS, particularly for individuals with low SCS and for Black or African American participants.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".