Interventional pulmonology: Focus on pulmonary diagnostics
Bibliographic record
Abstract
Interventional pulmonology (IP) allows comprehensive assessment of patients with benign and malignant airway, lung parenchymal and pleural disease. This relatively new branch of pulmonary medicine utilizes advanced diagnostic and therapeutic techniques to treat patients with pulmonary diseases. Endobronchial ultrasound revolutionized assessment of pulmonary nodules, mediastinal lymphadenopathy and lung cancer staging allowing minimally invasive, highly accurate assessment of lung parenchymal and mediastinal disease, with both macro- and microscopic tissue characterization including molecular signature analysis. High-spatial resolution, new endobronchial imaging techniques including autofluorescence bronchoscopy, narrow-band imaging, optical coherence tomography and confocal microscopy enable detailed evaluation of airways with increasing role in detection and treatment of malignancies arising in central airways. Precision in peripheral lesion localization has been increased through innovative navigational techniques including navigational bronchoscopy and electromagnetic navigation. Pleural diseases can be assessed with the use of non-invasive pleural ultrasonography, with high sensitivity and specificity for malignant disease detection. Medical pleuroscopy is a minimally invasive technique improving diagnostic safety and precision of pleural disease and pleural effusion assessment. In this review, we discuss the newest advances in diagnostic modalities utilized in IP, indications for their use, their diagnostic accuracy, efficacy, safety and challenges in application of these technologies in assessment of thoracic diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.008 | 0.005 |
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".