High-Resolution Computed Tomography and Survival in Fibrosing Alveolitis
Bibliographic record
Abstract
High-resolution computed tomography findings were reviewed in 32 patients with cryptogenic fibrosing alveolitis and 18 with fibrosing alveolitis associated with connective tissue diseases (other than scleroderma). The percentage of abnormal lung, total ground-glass attenuation, ground-glass attenuation not associated with fibrosis, and fibrosis (reticular pattern and honeycombing) were compared with lung function and survival. In cryptogenic fibrosing alveolitis, 1-year mortality tended to be higher in patients with predominantly fibrotic lesions (39%) compared to mainly ground-glass attenuation (11%). Similar results were obtained in fibrosing alveolitis associated with connective tissue disease (1-year mortality 44% for fibrosis versus 22% for ground-glass attenuation), but the differences were not statistically significant. In cryptogenic fibrosing alveolitis, the extent of abnormal lung on initial computed tomography was 67% ± 20% in survivors at 1 year and 86% ± 8% in nonsurvivors (p < 0.005); this difference was still significant at 4 years. In fibrosing alveolitis associated with connective tissue disease, the degree of lung involvement between survivors and nonsurvivors was different at 1 year only, thus other factors seem to determine survival. Ground-glass attenuation associated with fibrosis adversely affected survival in cryptogenic fibrosing alveolitis, in contrast to isolated ground-glass attenuation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".