September 2015 Tucson pulmonary journal club: genomic classifier for lung cancer
Bibliographic record
Abstract
No abstract available. Article truncated at 150 words. Silvestri GA, Vachani A, Whitney D, et al. A bronchial genomic classifier for the diagnostic evaluation of lung cancer. N Engl J Med. 2015;373(3):243-51. Pulmonary lesions are a common diagnostic dilemma for clinicians. Current literature describes the sensitivity of bronchoscopic techniques to be between 34 and 88%; which varies significantly depending on size and location of the biopsied lesion (1). Previously described gene expression patterns have been found to be associated with malignancy in healthy epithelial cells of the proximal airways\\(2). The primary aim of this study was to prospectively validate a specific gene expression classifier in patients undergoing bronchoscopic biopsy for suspected lung cancer. The study involved two independent, prospective, multicenter, observational studies (AEGIS-1 and AEGIS-2) conducted in the U.S., Canada and Ireland at 28 sites. Patients were excluded if they were never smokers, under age 21, or current cancer or former lung cancer patients. Patients were followed for ...
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 0.018 |
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".