Implementation of circulating tumour DNA multi-target mutation testing in plasma: a perspective from an external quality assessment providers’ survey
Bibliographic record
Abstract
Demand for large-scale tumour profiling across cancer types has increased in recent years, driven by the emergence of targeted drug therapies. Analysing alternations in plasma circulating tumour DNA (ctDNA) for cancer detection can improve survival; ctDNA testing is recommended when tumour tissue is unavailable. An online survey of molecular pathology testing was circulated by six external quality assessment members of IQN Path to registered laboratories and all IQN Path collaborative corporate members. Data from 275 laboratories across 45 countries were collected; 245 (89%) perform molecular pathology testing, including 177 (64%) which perform plasma ctDNA diagnostic service testing. The most common tests were next-generation sequencing-based (n = 113). Genes with known stratified treatment options, including KRAS (n = 97), NRAS (n = 84), and EGFR (n = 130), were common targets. The uptake of ctDNA plasma testing and plans to implement further testing demonstrates the importance of support from a well-designed EQA scheme.
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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 | 0.049 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".