Computed Tomography Findings in Pathological Usual Interstitial Pneumonia
Bibliographic record
Abstract
RATIONALE: Patients with a clinicopathological diagnosis of idiopathic pulmonary fibrosis (IPF) may have typical findings of usual interstitial pneumonia (UIP) on computed tomography (CT) or nonspecific or atypical findings, including those often seen in nonspecific interstitial pneumonia. OBJECTIVES: The aims of this study were to revisit the high-resolution CT findings of IPF and to clarify the correlation between the CT findings and mortality. METHODS: The study included 98 patients with a histologic diagnosis of UIP and a clinical diagnosis of IPF. Two observers evaluated the CT findings independently and classified each case into one of the following three categories: (1) definite UIP, (2) consistent with UIP, or (3) suggestive of alternative diagnosis. The correlation between the CT categories and mortality was evaluated using the Kaplan-Meier method and the log-rank test, as well as Cox proportional hazards regression models. MEASUREMENTS AND MAIN RESULTS: Thirty-three of the 98 CT scans were classified as definite UIP, 36 as consistent with UIP, 29 as suggestive of an alternative diagnosis. The mean survival was 45.7, 57.9, and 76.9 months, respectively. There was no significant difference in survival among the three categories (all P > 0.05). Traction bronchiectasis and fibrosis scores were significant predictors of outcome (hazard ratios: 1.30 and 1.10, respectively; 95% confidence intervals: 1.18-14.2 and 1.03-1.19, respectively). CONCLUSIONS: In patients with IPF and UIP pattern on the biopsy, the pattern of abnormality on thin-section CT, whether characteristic of UIP or suggestive of alternative diagnosis, does not influence prognosis. Prognosis is influenced by traction bronchiectasis and fibrosis scores.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".