Validation and reproducibility of a semi-qualitative food frequency questionnaire for assessment of sodium intake in Iranian population
Bibliographic record
Abstract
BACKGROUND: Few semi-quantitative food frequency questionnaires (SFFQ)s has yet been developed to assess sodium intake in Middle East region. This study was performed to validate a SFFQ for assessment of sodium consumption and food groups΄ contribution to sodium intake. METHODS: This study was performed on 219 healthy participants including 113 adults aged ≥19 years and 106 children aged 6-18 years in Isfahan, Iran. They were administered two SFFQ at the beginning and after 1 year to evaluate the reproducibility. The validity of SFFQ for assessment of sodium intake was compared with 24-h urine sodium and twelve 24-h dietary recalls which were completed monthly during a year as two standard methods. RESULTS: Correlation coefficient between the contribution of food groups to sodium intake based on SFFQ and 24-h dietary recalls varied from 0.04 for legumes (P = 0.667) to 0.47 for added salt (P < 0.001). There was a significant correlation between the estimated total sodium intake based on SFFQ and both standard methods (P < 0.01). Intraclass correlation coefficient (95% CI) between first and second SFFQ had a diverse range from 0.10 (-0.05, 0.17) for fats and oils to 0.49 (0.28, 0.69) for bread. According to the Bland-Altman plots, we observed an acceptable level of agreement between the two methods for sodium intake. CONCLUSIONS: The SFFQ was a relatively valid and reproducible method for estimating sodium intake. Combination of this SFFQ with a valid prediction of 24-h urinary sodium excretion can be useful in achieving more accurate results.
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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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".