The relationship between counselling methods after health check-ups and lipid profile improvement: a retrospective cohort study in Korea
Bibliographic record
Abstract
Abstract Background: Despite growing numbers of private health check-ups, it is not known whether post-check-up counselling and education can improve chronic disease management. It has previously been shown that, in general, these factors are crucial to chronic disease management. Therefore, this study aimed to determine which counselling methods should be employed, following private check-ups, for optimal chronic disease management.Methods: Participants were 7,039 adults over the age of 20, who received at least three check-ups from September 2013 to August 2019. All participants received the same form of counselling, three or more times consecutively. Three forms of counselling were examined: mail, telephone, and face-to-face. Chi-square tests, one-way analyses of variance, and Scheffé post-hoc analyses were performed to determine the relationship between various demographic characteristics and counselling methods received. We performed covariance analyses after adjusting for age, sex and number of examinations to determine the correlations between the counselling methods and changes in health indicators. When necessary, Bonferroni pairwise comparisons were performed.Results: The face-to-face counselling group was the oldest and had the poorest cardiometabolic parameters and glucose metabolic indices. However, face-to-face counselling was associated with the greatest improvement in levels of total cholesterol (P<0.001) and low-density lipoprotein cholesterol (P<0.001). Conclusion: Face-to-face counselling with doctors seems to be more effective at improving lipid profiles than phone or mail counselling. Further research is required to identify the effects of face-to-face counselling on long-term outcomes such as cardiovascular disease mortality. (IRB number: 1909-006-16282).
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".