Non-small cell lung cancer detection using microRNA expression profiling of bronchoalveolar lavage fluid and sputum.
Bibliographic record
Abstract
AIM: To assess if miRNA expression profiling of bronchoalveolar lavage (BAL) fluid and sputum could be used to detect early-stage non-small cell lung cancer (NSCLC). MATERIALS AND METHODS: Hierarchical cluster analysis was performed on the expression levels of 5 miRNAs (miR-21, miR-143, miR-155, miR-210, and miR-372) which were quantified using RNA reverse transcription and quantitative real-time polymerase chain reaction in sputum and BAL samples from NSCLC cases and cancer-free controls. RESULTS: Cluster analysis of the miRNA expression levels in BAL samples from 21 NSCLC cases and sputum samples from 10 cancer-free controls yielded a diagnostic sensitivity of 85.7% and specificity of 100%. Cluster analysis of sputum samples from the same patients yielded a diagnostic sensitivity of 67.8% and specificity of 90%. CONCLUSION: miRNA expression profiling of sputum and BAL fluids represent a potential means to detect early-stage NSCLC.
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How this classification was reachedexpand
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) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".