Clinical Features and Prediction Model of Secondary Infection Risk in Adult Patients With Chronic Respiratory Diseases: A Case-Control Study
Bibliographic record
Abstract
Background: There are limited investigations on the general pathogen features, clinical characteristics, and predicted clinical markers of secondary lower respiratory tract infection of chronic respiratory disorders. Methods: A total of 154 adult inpatients with chronic respiratory diseases between 2019 and 2022 were enrolled. Clinical data were retrospectively collected and analyzed. Multivariate logistic regression analysis was used to analyze the susceptibility factors of infection secondary to chronic respiratory diseases. Results: Among the patients with chronic respiratory diseases, the most prevalent condition was chronic obstructive pulmonary disease (44.2%, 68/154). Cough, expectoration, chest tightness, and wheezing were the predominant symptoms irrespective of infection. Pseudomonas aeruginosa accounted for 37% (20/54) in pathogen infection. Aspergillus fumigatus was the primary cause of filamentous fungal infection. The combination of low body mass index, increased tricuspid regurgitation pressure, and decreased lymphocyte count could accurately predict infection secondary to chronic respiratory diseases (area under curve (AUC): 0.788, 95% confidence interval (CI): 0.689 - 0.887, P = 0.000). Conclusions: This study focused and explored the common features between secondary infections of various chronic respiratory diseases. The prediction model is expected to enable timely detection and treatment of secondary infections in clinical practice.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".