An Overview of Comprehensive Genomic Profiling Technologies to Inform Cancer Care
Bibliographic record
Abstract
Horizon Scan reports provide brief summaries of information regarding new and emerging health technologies; Heath Technology Update articles typically focus on a single device or intervention. This Horizon Scan summarizes the available information regarding emerging comprehensive genomic profiling (CGP) technologies for informing cancer treatments. These technologies are based on next-generation sequencing platforms, which can characterize up to hundreds of genes and other genomic information with a single sample. Emerging tests are also compatible with minimally invasive liquid biopsies that use fluids such as blood samples to support clinical decision-making. CGP could be an alternative or a complement to conventional testing that uses single-biomarker assays or limited gene panels. Some emerging CGP tests available in Canada, the US, and Europe are being considered to inform the treatment of non–small cell lung cancer (NSCLC) because it has the highest number of identified biomarkers. Most identified studies have examined CGP use with NSCLC. The emerging evidence about the clinical and cost-effectiveness of CGP technologies for either NSCLC or other cancer types remains uncertain. Without randomized trials and robust study designs, it is not yet well-established whether the additional costs and technical requirements of CGP may provide better clinical outcomes compared with conventional molecular testing. This Horizon Scan also provides considerations for health systems about testing infrastructure, training for health care professionals, and understanding different patients’ perspectives should CGP or other next-generation sequencing technologies become more widely used in Canada.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".