Pathologic Subgroups of Nonspecific Interstitial Pneumonia
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To determine whether the subtypes of nonspecific interstitial pneumonia (NSIP) could be differentiated from other idiopathic interstitial pneumonias (IIPs) on the basis of findings on high-resolution computed tomography (CT). METHODS: Two observers evaluated the high-resolution CT findings in 90 patients with IIPs. The patients included 36 with NSIP, 11 with usual interstitial pneumonia (UIP), 8 with cryptogenic organizing pneumonia (COP), 10 with acute interstitial pneumonia (AIP), 14 with desquamative interstitial pneumonia (DIP) or respiratory bronchiolitis-associated interstitial lung disease (RB-ILD), and 11 with lymphoid interstitial pneumonia (LIP). The NSIP cases were subdivided into group 1 NSIP (n = 6), group 2 NSIP (n = 15), and group 3 NSIP (n = 15). RESULTS: Observers made a correct diagnosis with a high level of confidence in 65% of NSIP cases, 91% of UIP cases, 44% of COP cases, 40% of AIP cases, 32% of DIP or RB-ILD cases, and 82% of LIP cases. Group 1 NSIP was misdiagnosed as AIP, DIP or RB-ILD, and LIP in 8.3% of patients, respectively. Group 2 NSIP was misdiagnosed as COP in 10% of patients, LIP in 6.7%, AIP in 3.3%, and DIP or RB-ILD in 3.3%. Group 3 NSIP was misdiagnosed as UIP in 6.7% of patients, COP in 6.7%, and DIP or RB-ILD in 3.3%. CONCLUSIONS: In most patients, NSIP can be distinguished from other IIPs based on the findings on high-resolution CT. Only a small percentage of patients with predominantly fibrotic NSIP (group 3 NSIP) show overlap with the high-resolution CT findings of UIP.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it