Expert Elicitation for the Judgment of Prion Disease Risk Uncertainties
Bibliographic record
Abstract
There is a high level of uncertainty surrounding the potential for iatrogenic prion transmission through transplantation, medical instrument reuse, blood transfusion, and blood product use due to a lack of evidence-based research on this important risk issue. A group of specialists was enlisted to evaluate some of the knowledge gaps in this area using the "Classical Model," a structured elicitation procedure for weighting and pooling expert judgment. The elicitation exercise was undertaken in March 2009 with 11 transmissible spongiform encephalopathy (TSE) experts who were first calibrated using a series of seed questions for which the answers are known; they were then asked to answer a number of target questions that are important for risk assessment purposes, but for which there remains high uncertainty at this time. The target questions focused on variant Creutzfeldt-Jakob disease (vCJD) prevalence, incubation times for vCJD, genetic susceptibility to prion disease, blood infectivity, prion reduction of blood and blood products, surgical instrument risks, and interspecies transmission of TSEs. The experts were also asked to perform pairwise risk rankings for 12 different potential routes of infection. Dura mater transplantation was seen as having the highest risk, while dental tissue grafts were viewed as presenting the lowest risk of iatrogenic transmission. The structured elicitation procedure provides a rational, auditable, and repeatable basis for obtaining useful information on prion disease risk issues, for which data are sparse.
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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 | 0.079 | 0.201 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".