Presence of an EML4-ALK gene fusion detected by microfluidic chip DNA hybridization
Bibliographic record
Abstract
Non-small cell lung cancer (NSCLC) accounts for ∼80-85% of all lung cancer cases, and the EML4-ALK fusion oncogene is a well-known contributor to NSCLC cases. Expensive methods such as FISH, IHC, and NGS have been used to detect the EML4-ALK fusion oncogene. Here, a cost-effective and facile method of detecting and differentiating an EML4-ALK fusion oncogene from the wild-type gene has been accomplished by DNA hybridization using the microfluidic biochip. First, oligonucleotide probes were confirmed for successful detection of immobilized sense strands. Second, capture of the sense PCR product strands (fusion and WT) and their subsequent detection and differentiation were accomplished. Our proof-of-concept study shows the ability to detect 1% fusion products, among WT ones.
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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 | 0.000 | 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 | 0.001 | 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".