Talking with your patients about dietary cholesterol, diet and nutrition: best practices for family physicians
Bibliographic record
Abstract
BACKGROUND: Family physicians, as gatekeepers for the healthcare system, are at the forefront of detecting, diagnosing, and managing diseases. When it comes to coronary heart disease (CHD), patients expect them to be knowledgeable about ways to reduce CHD risk, such as through diet. Unfortunately, most family physicians have not received formal training about nutritional counselling for patients. There is also little time or incentive to educate patients about diet. RESULTS: This has led to the preference for referring patients for dietary counselling. However, when properly informed, family physicians can be effective conduits for dietary counselling. A Danish study showed that patients at risk for CHD who were given nutritional counselling by family physicians experienced significant weight loss, reduction of waist circumference and body mass index, a significant increase in high-density lipoprotein cholesterol, and a significant reduction in the risk of developing cardiovascular disease. CONCLUSIONS: By providing nutritional counselling, physicians can serve as a source for current information and help to correct long-held misperceptions that patients may have regarding diet.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".