Managing regulatory issues arising from new diagnostic technologies: High throughput sequencing as a case study
Bibliographic record
Abstract
Abstract New diagnostic technologies such as high throughput sequencing (HTS) are powerful tools that are used to detect and identify a broad range of biological organisms. As a relatively new diagnostic technology, HTS generates large volumes of data in multiple formats that require technical expertise to interpret and action accurately. Significantly, HTS can detect previously unknown organisms, often with no known associated biological parameters. Caution is required by regulatory authorities; guidelines and decision making flowcharts need to be developed to ensure appropriate and consistent diagnoses and consistent and confident decision making. This article explores the challenges involved in making regulatory decisions based on HTS data; discusses considerations that should be accounted for when managing these regulatory issues; makes suggestions to inform regulatory decisions; and presents case studies that demonstrate the potential advantages of HTS in identifying various plant pests, and the associated regulatory implications. Three categories of HTS-related diagnostics from which regulatory actions are drawn include: detecting specific pests; screening plants with symptoms but no known pests detected using conventional methods or without any prior screening; and screening plants that do not show obvious symptoms, and where the intent of the diagnostic method is investigational or regulatory in nature, such as demonstrating freedom from a regulated pest for market access.
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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 | 0.041 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".