Dietary Calcium Intake Assessment by Short Food Frequency Questionniare in Thais Adults Living in Chiang Mai, Thailand
Bibliographic record
Abstract
The aims of this study were to develop and validate the short food frequency questionnaire (sFFQ) to assess calcium intake from food in Thais person for use in clinical practice or community pharmacy. Data collection was performed during November 2009 to January 2010. The sFFQ consisted of 33 item of food. The frequency of food intake in sFFQ food list was interviewed by investigator. Seven day after subject were interviewed by sFFQ, subjects were asked to fill out the Three Day Dietary Record (3DR) for 3 day. Daily calcium intake assessed by both methods was calculated by using INMUCAL software, which was developed by the Institute of Nutrition, Mahidol University. The 131 subjects who completed sFFQ and 3DR had mean age of 24.4 years, 71.3% were female. The mean daily calcium intake assessed by sFFQ and 3DR were 692.0 + 524.9 mg and 477.4 + 261.9 mg, respectively. The mean daily calcium intakes assessed by sFFQ were significantly higher than 3DR (p < 0.05). The Spearman’s correlation coefficient between calcium intakes assessed by the two methods was 0.18 (p <0.05). In conclusion, the newly developed sFFQ was a suitable tool for the determination of calcium intakes in Thais adults. The next step in assessing the validity of this sFFQ will be its use in clinical setting such as community pharmacy or out-patient clinic.
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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 | 0.002 |
| 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".