Consensus Recommendations to Optimize the Detection and Reporting of NTRK Gene Fusions by RNA-Based Next-Generation Sequencing
Bibliographic record
Abstract
The detection of gene fusions by RNA-based next-generation sequencing (NGS) is an emerging method in clinical genetic laboratories for oncology biomarker testing to direct targeted therapy selections. A recent Canadian study (CANTRK study) comparing the detection of NTRK gene fusions on different NGS assays to determine subjects’ eligibility for tyrosine kinase TRK inhibitor therapy identified the need for recommendations for best practices for laboratory testing to optimize RNA-based NGS gene fusion detection. To develop consensus recommendations, representatives from 17 Canadian genetic laboratories participated in working group discussions and the completion of survey questions about RNA-based NGS. Consensus recommendations are presented for pre-analytic, analytic and reporting aspects of gene fusion detection by RNA-based NGS.
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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 | 0.029 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.011 |
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".