Circulating Fibrocytes Are an Indicator of Poor Prognosis in Idiopathic Pulmonary Fibrosis
Bibliographic record
Abstract
RATIONALE: The clinical management of idiopathic pulmonary fibrosis (IPF) remains a major challenge due to lack of effective drug therapy or accurate indicators for disease progression. Fibrocytes are circulating mesenchymal cell progenitors that are involved in tissue repair and fibrosis. OBJECTIVES: To test the hypothesis that assay of these cells may provide a biomarker for activity and progression of IPF. METHODS: Fibrocytes were defined as cells positive for CD45 and collagen-1 by flow cytometry and quantified in patients with stable IPF and during acute exacerbation of the disease. We investigated the clinical and prognostic value of fibrocyte counts by comparison with standard clinical parameters and survival. We used healthy age-matched volunteers and patients with acute respiratory distress syndrome as control subjects. MEASUREMENTS AND MAIN RESULTS: Fibrocytes were significantly elevated in patients with stable IPF (n = 51), with a further increase during acute disease exacerbation (n = 7; P < 0.001 vs. control subjects). Patients with acute respiratory distress syndrome (n = 10) were not different from healthy control subjects or stable patients with IPF. Fibrocyte numbers were not correlated with lung function or radiologic severity scores, but they were an independent predictor of early mortality. The mean survival of patients with fibrocytes higher than 5% of total blood leukocytes was 7.5 months compared with 27 months for patients with less than 5% (P < 0.0001). CONCLUSIONS: Fibrocytes are an indicator for disease activity of IPF and might be useful as a clinical marker for disease progression. This study suggests that quantification of circulating fibrocytes may allow prediction of early mortality in patients with IPF.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".