Bibliographic record
Abstract
Horizon Scan reports provide brief summaries of information regarding new and emerging health technologies; these technologies are identified through the CADTH Horizon Scanning Service as topics of potential interest to health care decision-makers in Canada. This report is not an endorsement or assessment of any test or technology. This Horizon Scan summarizes the available information regarding the emerging technology of liquid biopsy–based, multi-cancer early detection tests for cancer screening. This Horizon Scan focuses specifically on the Galleri (GRAIL Inc.) and CancerSEEK (Exact Sciences) tests, which are further along in the development cycle and are being assessed in different international clinical studies. Multi-cancer early detection technologies aim to provide a new approach to complement traditional cancer screening programs. These tests examine genetic signals within blood samples with next-generation sequencing and computational algorithms to assess the presence and type of different cancers. Research to date has focused on describing results from training and validation studies that have provided initial estimates of test performance and modelling studies estimating the potential impact on cancer incidence. This Horizon Scan also highlights some issues for health care decision-makers to consider about the technology relating to real-world test performance, the potential benefits and harms of screening with multi-cancer early detection tests, and the disruptiveness to health systems they could pose. Ongoing review of clinical trials and the emerging evidence base can help inform health systems in Canada about their potential role within cancer-control initiatives.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".