Managing regulatory issues arising from new diagnostic technologies: High throughput sequencing as a case study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it