Role of imaging in the diagnosis of diffuse and interstitial lung diseases
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review aims to provide a literature update focused on the role of imaging in the diagnosis, prognosis and quantification of interstitial lung diseases (ILDs). Special emphasis is given in the management of atypical cases and in the multidisciplinary team approach in reaching the diagnosis of the various ILDs. RECENT FINDINGS: Accumulated knowledge on imaging has increased the accuracy of differential diagnosis in atypical cases, in overlap of findings and in secondary as opposed to idiopathic ILDs. There is increasing awareness about interstitial lung abnormalities in smokers' lungs from lung cancer screening and indirect evidence of linkage of fibrosis and smoking. Improvement in radiologic-pathologic correlation reveals less typical high-resolution computed tomography patterns to be predictive of pulmonary fibrosis. Major diagnostic criteria such as honeycombing may be hampered by the coexistence of emphysema. High-resolution computed tomography may predict clinical outcome and survival of patients in ILDs and is a decision maker in the multidisciplinary approach of diagnosis. SUMMARY: High-resolution computed tomography plays a crucial role in the diagnosis, prognosis, quantification and monitoring of ILDs. It provides a definite noninvasive diagnosis in typical findings and helps in reaching the most accurate diagnosis in a multidisciplinary discussion in equivocal cases.
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 | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".