Design, implementation, and interpretation of amplification studies for prion detection
Bibliographic record
Abstract
Amplification assays for transmissible spongiform encephalopathies have been in development for close to 15 years, with critical implications for the postmortem and antemortem diagnosis of human and animal prion diseases. Little has been published regarding the structured development, implementation and interpretation of experiments making use of protein misfolding cyclic amplification (PMCA) and real time quaking-induced conversion (RT-QuIC), and our goal with this Perspectives manuscript is to offer a framework which might allow for more efficient expansion of pilot studies into diagnostic trials in both human and animal subjects. This framework is made up of approaches common to diagnostic medicine, including a thorough understanding of analytical and diagnostic sensitivity and specificity, an a priori development of amplification strategy, and an effective experimental design. It is our hope that a structured framework for prion amplification assays will benefit not only experiments seeking to sensitively detect naturally-occurring cases of prion diseases and describe the pathogenesis of TSEs, but ultimately assist with future endeavors seeking to use these methods more broadly for other protein misfolding disorders, including Alzheimer's and Parkinson's disease.
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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 | 0.031 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".