The relationship of systemic inflammation to prior hospitalization in adult patients with cystic fibrosis
Bibliographic record
Abstract
Background: In cystic fibrosis (CF) patients, it has been suggested that systemic inflammation may be an important risk factor for poor health outcomes. The relationship of plasma inflammatory biomarkers to lung function and hospitalization history remains largely unexplored. Methods: This cross-sectional study included 58 consecutive, clinically stable adults from the CF Clinic at St. Paul's Hospital (Vancouver, Canada). Blood levels of interleukin (IL)-6, IL-1β, C-reactive protein (CRP), interleukin (IL)-6, IL-1β, granzyme B (GzmB), chemokine C-C motif ligand 18 (CCL18/PARC), surfactant protein D (SP-D), lipopolysaccharide (LPS)-binding protein, and soluble cluster of differentiation 14 (sCD14) were measured using enzyme-linked immunosorbent assays, and LPS levels were measured using a Limulus amebocyte lysate assay. Spirometry was also performed. Multivariable linear regression analysis was used to assess relationships of the blood biomarkers to lung function. Results: Lung function impairment was independently associated with elevated plasma levels of CRP (P < 0.01), IL-6 (P = 0.04), IL-1β (P < 0.01), and LBP (P < 0.01). Increasing age (P < 0.01), reduced body mass index (P = 0.02), prior hospitalizations (P = 0.03), and presence of Pseudomonas aeruginosa in sputum cultures (P < 0.01) were also associated with reduced lung function. Elevated concentrations of LPS in plasma were associated with a previous history of hospitalization (P < 0.05). There was a trend towards an increase in plasma IL-6 (P = 0.07) and IL-1β (P = 0.06) levels in patients who were previously hospitalized. Conclusions: IL-6 and IL-1β are promising systemic biomarkers for lung function impairment and history of hospitalization in adult patients with CF.
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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 | 0.002 |
| 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 | 0.001 |
| 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".