Nonspecific Interstitial Pneumonia and Idiopathic Pulmonary Fibrosis: Changes in Pattern and Distribution of Disease over Time
Bibliographic record
Abstract
PURPOSE: To retrospectively assess the change in disease pattern of nonspecific interstitial pneumonia (NSIP) and idiopathic pulmonary fibrosis (IPF) findings seen at thin-section computed tomography (CT) at long-term follow-up and to compare the same with initial findings at CT. MATERIALS AND METHODS: The study included 48 patients (28 men, 20 women; mean age, 57.5 years) with biopsy-proved NSIP (n = 23) or IPF (n = 25) who underwent CT at initial diagnosis and at follow-up 34-155 months later. The CT scans were randomized and reviewed by two independent thoracic radiologists for pattern and distribution of ground-glass opacity (GGO), reticulation, traction bronchiectasis and bronchiolectasis, and honeycombing. Statistical analysis was performed by using nonparametric methods and univariate logistic regression. RESULTS: Follow-up CT in patients with NSIP showed marked decrease in the extent of GGO, increase in reticulation, and a greater likelihood of peripheral distribution (all P < .05). At presentation, the CT findings were interpreted as suggestive of NSIP in 18 of 23 patients with NSIP and indeterminate or suggestive of IPF in five. In five (28%) of 18 patients with initial findings suggestive of NSIP, the follow-up CT scans were interpreted as more suggestive of IPF. No CT features seen at presentation allowed distinction between patients with NSIP that maintained an NSIP pattern at follow-up and those that progressed to an IPF pattern. CONCLUSION: At follow-up CT, 28% of patients with initial CT findings suggestive of NSIP progressed to findings suggestive of IPF. Similar initial CT findings for NSIP may have different imaging outcomes.
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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 | 0.004 |
| 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 | 0.001 |
| 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".