Differences between Staphylococcus aureus nasal carriage and IgE-sensitization to Staphylococcus aureus enterotoxin on risk factors and effects in adult population
Bibliographic record
Abstract
BACKGROUND: Staphylococcus aureus (SA) nasal carriage (SA carriage) and IgE-sensitization to SA enterotoxin (SE IgE-sensitization) are known to be associated with chronic airway disease. OBJECTIVE: This study aimed to evaluate the differences in risk factors, type 2 inflammation and respiratory symptoms between SA carriage and SE IgE-sensitization. METHODS: We conducted a cross-sectional study of a community-based adult population to evaluate the environmental exposure and health impact of the Pohang Industrial Complex, Korea. Participants were examined based on self-reported questionnaires, nasal swab, and blood sampling. RESULTS: There were 307 participants, and the overall prevalence of SA carriage and SE IgE-sensitization was 26.1% (80/307) and 25.7% (79/307), respectively. An urban environment was significantly correlated with SA carriage, whereas age and obesity were significantly correlated with SE IgE-sensitization. SA carriage was not associated with an increase in total IgE and blood eosinophil count, whereas SE IgE-sensitization was associated with an increased total IgE and blood eosinophil count. SA carriage was significantly correlated with cough persisting for more than three weeks (OR, 3.044; 95% CI, 1.137-8.153) and sputum (OR, 2.429; 95% CI, 1.008-5.854). SE IgE-sensitization was a significant correlation with only sputum (OR, 2.452; 95% CI, 1.066-5.640). SA carriage and SE IgE-sensitization showed a synergistic effect on the prevalence of cough and sputum. CONCLUSION: SA carriage was associated with the urban environment, and SE IgE-sensitization was associated with the elderly and obesity. SA carriage and SE IgE-sensitization had different correlation with type 2 inflammation and airway symptoms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".