Bibliographic record
Abstract
Bronchoscopy is a minimally invasive method for diagnosis of diseases of the airways and the lung parenchyma. Standard bronchoscopy uses the reflectance/scattering properties of white light from tissue to examine the macroscopic appearance of airways. It does not exploit the full spectrum of the optical properties of bronchial tissues. Advances in optical imaging such as optical coherence tomography (OCT), confocal endomicroscopy, autofluorescence imaging and laser Raman spectroscopy are at the forefront to allow in vivo high-resolution probing of the microscopic structure, biochemical compositions and even molecular alterations in disease states. OCT can visualize cellular and extracellular structures at and below the tissue surface with near histological resolution, as well as to provide three-dimensional imaging of the airways. Cellular and subcellular imaging can be achieved using confocal endomicroscopy or endocytoscopy. Contrast associated with light absorption by haemoglobin can be used to highlight changes in microvascular structures in the subepithelium using narrow-band imaging. Blood vessels in the peribronchial space can be displayed using Doppler OCT. Biochemical compositions can be analysed with laser Raman spectroscopy, autofluorescence or multispectral imaging. Clinically, autofluorescence and narrow-band imaging have been found to be useful for localization of preneoplastic and neoplastic bronchial lesions. OCT can differentiate carcinoma in situ versus microinvasive cancer. Endoscopic optical imaging is a promising technology that can expand the horizon for studying the pathogenesis and progression of airway diseases such as COPD and asthma, as well as to evaluate the effect of novel therapy.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.014 |
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".