Phenotyping airway disease with optical coherence tomography
Bibliographic record
Abstract
Airway diseases are a major concern around the world. However, the pace of new drug and biomarker discovery has lagged behind those of other common disorders such as cardiovascular diseases and diabetes. One major barrier in airway research has been the inability to accurately visualize large or small airway remodelling or dysplastic/neoplastic (either pre or early cancerous) changes using non- or minimally invasive instruments. The advent of optical coherence tomography (OCT) has the potential to revolutionize airway research and management by allowing investigators and clinicians to visualize the airway with resolution approaching histology and without exposing patients to harmful effects of ionizing radiation. Thus, with the aid of OCT, we may be able to accurately determine and quantify the extent of airway remodelling in asthma and chronic obstructive pulmonary disease, detect early pre-cancerous lesions in smokers for chemoprevention, study the upper airway anatomy of patients with obstructive sleep apnea in real time while they are asleep and facilitate optimal selection of stents for those with tracheal obstruction. In this paper, we review the current state of knowledge of OCT and its possible application in airway 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 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".