Epidemiology of Chronic Inflammatory Demyelinating Polyneuropathy in South Korea: A Population-Based Study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: We performed a population-based study to determine the prevalence and incidence of chronic inflammatory demyelinating polyneuropathy (CIDP) in South Korea using data from the Korean Health Insurance Review and Assessment Service (HIRA) database. METHODS: Data recorded in the HIRA database between January 2016 and December 2020 were analyzed. The inclusion criteria in this study for patients with CIDP were a diagnostic code of G61.8 in the seventh and eighth revision of the Korean Standard Classification of Disease and a >3-month history of oral immunosuppressant use. The age-adjusted incidence rate and prevalence of CIDP in South Korea were also analyzed. RESULTS: CIDP was newly diagnosed in 953 patients during the study period. The mean age at diagnosis was 58.36 years, and the male-to-female ratio was 1.74. The age-adjusted incidence rates were 0.22, 0.21, 0.23, 0.30, and 0.25 per 100,000 person-years in 2016, 2017, 2018, 2019, and 2020, respectively. The age-adjusted prevalence was estimated at 1.16 per 100,000 persons in 2020. Age and the Elixhauser Comorbidity Index were associated with the in-hospital mortality of patients with CIDP. Infection and cardiovascular disease (CVD) were also significantly associated with the in-hospital mortality of those patients. Acute-onset CIDP was initially diagnosed in an estimated 101 out of 953 patients with CIDP. CONCLUSIONS: The prevalence and incidence rates of CIDP in South Korea were comparable between this nationwide cohort study and previous studies. Common comorbidities such as CVD and diabetes should be appropriately monitored in patients with CIDP to prevent a poor prognosis and socioeconomic burden.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".