Comparative manifestations and diagnostic accuracy of high-resolution computed tomography in usual interstitial pneumonia and nonspecific interstitial pneumonia
Bibliographic record
Abstract
PURPOSE OF REVIEW: Of the idiopathic interstitial pneumonias, the differentiation between idiopathic pulmonary fibrosis (IPF) and nonspecific interstitial pneumonitis (NSIP) raises considerable diagnostic challenges, as their clinical presentations share many overlapping features. IPF is a fibrosing pneumonia of unknown cause, showing a histologic pattern of usual interstitial pneumonia (UIP), and has a poorer prognosis than does NSIP. This review examines whether the radiographic features of IFP and NSIP as assessed by high-resolution computed tomography (HRCT) can be used to distinguish between these two entities. RECENT FINDINGS: The diagnostic accuracy of HRCT for UIP and NSIP has been reported to be approximately 70% in various studies. Disagreement between the HRCT diagnosis and the histologic diagnosis occurs in approximately one-third of the cases. The predominant feature of honeycombing on HRCT yields a specificity of approximately 95% and sensitivity of approximately 40% for UIP. In contrast, a predominant feature of ground glass opacities (GGOs) gives a sensitivity of approximately 95% and specificity of approximately 40% for NSIP. SUMMARY: The finding of honeycombing as the predominant HRCT feature suggests the diagnosis of UIP and may exclude the need for biopsy. Predominant features of GGOs are not specific enough to distinguish between NSIP and UIP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".