Interventional pulmonology: Focus on pulmonary diagnostics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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