Localization of Bronchial Intraepithelial Neoplastic Lesions by Fluorescence Bronchoscopy*
Bibliographic record
Abstract
Background: In the treatment of lung cancer, the best outcome is achieved when the lesion is discovered in the intraepithelial (preinvasive) stage. However, intraepithelial neoplastic lesions are difficult to localize by conventional white-light bronchoscopy (WLB). Objective: To determine if autofluorescence bronchoscopy, when used as an adjunct to WLB, could improve the bronchoscopist's ability to locate and remove biopsy specimens from areas suspicious of intraepithelial neoplasia as compared with WLB alone. Method: A multicenter clinical trial was conducted in seven institutions in the United States and Canada. WLB followed by fluorescence examination with the light-induced fluorescence endos¬ copy (LIFE) device was performed in 173 subjects known or suspected to have lung cancer. Biopsy specimens were taken from all areas suspicious of moderate dysplasia or worse on WLB and/or LIFE examination. In addition, random biopsy specimens were also taken from other parts of the bronchial tree. Results: The relative sensitivity ofWLB+LIFE vs WLB alone was 6.3 for intraepithelial neoplastic lesions and 2.71 when invasive carcinomas were also included. The positive predictive value was 0.33 and 0.39 and the negative predictive value was 0.89 and 0.83, respectively, for WLB+LIFE
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".