The Unholy Grail of cancer screening: or is it just about the Benjamins?
Bibliographic record
Abstract
The biotechnology company Grail developed a non-invasive blood test (Galleri test) which is claimed to detect 50 types of cancer at early and potentially curable stages. The initially promising results from prospective studies, and the anticipated financial success of Grail led the sequencing giant Illumina to purchase Grail for $8 billion (2021). Following this event, Grail collaborated with the UK National Health System to further clarify the test's capability, in a 3-year prospective trial, along with the standard of care. The UK-NHS announced that the trial will provide a clearer understanding of the efficacy of the Galleri test within the NHS framework. If the test does not perform as expected, valuable insights will still be gained to guide future research aimed at enhancing cancer screening. We previously expressed concerns about the sensitivity and specificity of the Galleri test. In this opinion paper, we revisit the hyped technology, and we provide new suggestions on the use of this test.
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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 | 0.023 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.013 | 0.024 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".