The effect of coffee consumption on serum total cholesterol in the Sami and Norwegian populations
Bibliographic record
Abstract
OBJECTIVE: To assess coffee consumption in the Sami and Norwegian populations and to investigate the impact of unfiltered boiled coffee consumption on serum cholesterol concentrations. DESIGN: A cross-sectional study. Information was collected by self-administrated questionnaires and total serum cholesterol was analysed. Participants were divided into three ethnic groups: Sami I (Sami used as home language in the last three generations), Sami II (at least one Sami identity marker) and Norwegian. SETTING: In an area with Sami, Kven/Finnish and Norwegian populations, the SAMINOR study, 2003-2004. SUBJECTS: A total of 5647 men and 6347 women aged 36-79 years. RESULTS: More than 90 % of the study populations were coffee drinkers. Only 22 % were unfiltered coffee consumers. Sami I had the highest proportion of participants who consumed nine or more cups of unfiltered coffee per day, although the number of participants was limited. Total coffee consumption was associated with increased total cholesterol for men (P < 0·01) and women (P < 0·0001). For those who drank only unfiltered coffee, a significant association was found only in Norwegian men, adjusted for physical activity in leisure time, BMI and smoking habits (P < 0·001). From the lowest (less than five cups) to the highest (nine or more cups) unfiltered coffee consumption category, the mean total cholesterol levels increased by 0·29 mmol/l in Norwegian men. CONCLUSIONS: Unfiltered coffee consumption was lower in the present study compared to previous reports. In general, total coffee consumption was positively associated with total cholesterol levels. However, for unfiltered coffee consumption, an association was found only in Norwegian men.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".