Regular follow-up visits reduce the risk for asthma exacerbation requiring admission in Korean adults with asthma
Bibliographic record
Abstract
Asthma requires regular follow-up visits and sustained medication use. Although several studies have reported the importance of adherence to medication and compliance with the treatment, none to date have reported the importance of regular follow-up visits. We investigated the effects of regular clinical visits on asthma exacerbation. We used claims data in the national medical insurance review system provided by the Health Insurance Review and Assessment Service of Korea. We included subjects aged ≥ 15 years with a diagnosis of asthma, and who were prescribed asthma-related medication, from July 2013 to June 2014. Regular visitors (frequent visitors) were defined as subjects who visited the hospital for follow-up of asthma three or more times per year. Among 729,343 subjects, 496,560 (68.1%) were classified as regular visitors. Old age, male sex, lack of medical aid insurance, attendance of a tertiary hospital, a high Charlson comorbidity index, and a history of admission for exacerbated asthma in the previous year were significant determining factors for regular visitor status. When we adjusted for all these factors, frequent visitors showed a lower risk of asthma exacerbation requiring general ward admission (odds ratio [OR] 0.48; 95% confidence interval [CI] 0.47–0.50; P < 0.001), emergency room admission (OR 0.83; 95% CI 0.79–0.86; P < 0.001), and intensive care unit admission (OR 0.49; 95% CI 0.44–0.54; P < 0.001) than infrequent visitors. Regular clinical visits are significantly associated with a reduced risk of asthma exacerbation requiring hospital admission in Korean adults with asthma.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".