Bronchitis, COPD, and pneumonia after viral endemic of patients with leprosy on Sorok Island in South Korea
Bibliographic record
Abstract
Viral respiratory diseases (VRDs) cause lung inflammation and inflammatory cytokine production. We study whether dapsone is responsible for its observed preventive treatment effects of the sustained viral RNA interferon response. Around 2008 and 2012, Korea's Dementia Management Act stipulated drastic changes in the administration of dementia medication by medical staff. Participants were randomized and we compared leprosy patients with VRDs after prescribing dapsone as a standard treatment from 2005 to 2019. Significance was evaluated based on the dapsone-prescribed (+) subgroup and the dapsone-unprescribed (-) subgroup of the VRD diagnosed (+) and VRD undiagnosed (-) subgroup. We analyzed VRD ( +)/(- with dapsone (+)/(-) group and used a T-test, and designed the equation of acetylation with dapsone and acetylcholine (AA) equation. The 6394 VRD participants who received the dapsone intervention compared to the 3255 VRD participants in the control group demonstrated at T2 VRD (+) dapsone (-) (mean (M) = 224.80, SD = 97.50): T3 VRD (-) dapsone (+) (M = 110.87, SD = 103.80), proving that VRD is low when dapsone is taken and high when it is not taken. The t value is 3.10, and the p value is 0.004395 (significant at p < 0.05). After an increase in VRDs peaked in 2009, bronchitis, COPD, and pneumonia surged in 2013. The AA equation was strongly negatively correlated with the prevalence of bronchitis and chronic obstructive pulmonary disease (COPD): with bronchitis, r(15) = -0.823189, p = 0.005519, and with COPD, r(15) = -0.8161, p = 0.000207 (significant at p < 0.05). Dapsone treated both bronchitis and COPD. This study provides theoretical clinical data to limit acetylcholine excess during the VRD pandemic for bronchitis, COPD, and pneumonia.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".