Challenges to Innovation Arising from Current Companion Diagnostic Regulations and Suggestions for Improvements
Bibliographic record
Abstract
A companion diagnostic is a diagnostic test that provides information essential for the safe and effective use of a corresponding therapeutic product. To obtain marketing approval, the companion diagnostic must demonstrate acceptable analytical and clinical performance. Companion diagnostic regulations are intended to protect patients by ensuring quality and consistency of treatment-guiding biomarker testing in clinical trials and clinical practice. However, current regulations have had unintended negative consequences relating to innovation, implementation, and accessibility of precision medicine; increasing complexity and cost burden; and inhibiting development of novel diagnostics and biomarker-targeted therapeutics. We propose a range of practical solutions to these challenges, advocating that regulators, pharmaceutical companies, molecular pathologist groups, and diagnostic companies work together to increase flexibility and promote diagnostic innovation, while maintaining high-quality diagnostic testing to ensure all patients get the most appropriate treatments.
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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 | 0.028 | 0.072 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.024 | 0.038 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".