Contribution of foods to <i>trans</i> unsaturated fatty acid intake in a group of Irish adults
Bibliographic record
Abstract
OBJECTIVE: To assess fat intake with particular focus on trans unsaturated fatty acid (TUFA) intake and the major sources of TUFA among Irish individuals using a Fat Intake Questionnaire (FIQ), designed specifically for an Irish context. SUBJECTS AND METHODS: A total of 105 healthy volunteers (43 females, 62 males; aged 23-63 years) were recruited from Dublin Airport Medical centre, Republic of Ireland. Dietary intake was assessed using an 88 food item/food group semi-quantitative FIQ, which was developed and validated for the Irish population. RESULTS: Mean energy intake was 10.6 MJ day(-1), and 34% was provided by fat. Saturated, monounsaturated, polyunsaturated, trans unsaturated fatty acids and linoleic acid contributed 13%, 10%, 6%, 2% and 5% of energy respectively. Mean TUFA intake was 5.4 g day(-1) (range 0.3-26). Margarine spreads provided the majority of TUFAs (1.93 g day(-1)), but the contribution was significantly greater for men compared with women (2.35 g day(-1) versus 1.33 g day(-1); P = 0.024). Milk and meat also contributed more to TUFA intake for men compared with women, but confectionery was a significantly greater contributor for women (8.6% versus 3.1% respectively, P = 0.01). CONCLUSIONS: Although the mean TUFA intake of the total group was 5.4 g day(-1) and was within current dietary recommendations (2% energy intake), some individuals had intakes as high as 26 g day(-1). Public health efforts are therefore required to reduce TUFA intake in those individuals with high intakes.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".