Diagnostic Accuracy of Prion Disease Biomarkers in Iatrogenic Creutzfeldt-Jakob Disease
Bibliographic record
Abstract
Human prion diseases are classified into sporadic, genetic, and acquired forms. Within this last group, iatrogenic Creutzfeldt-Jakob disease (iCJD) is caused by human-to-human transmission through surgical and medical procedures. After reaching an incidence peak in the 1990s, it is believed that the iCJD historical period is probably coming to an end, thanks to lessons learnt from past infection sources that promoted new prion prevention and decontamination protocols. At this point, we sought to characterise the biomarker profile of iCJD and compare it to that of sporadic CJD (sCJD) for determining the value of available diagnostic tools in promptly recognising iCJD cases. To that end, we collected 23 iCJD samples from seven national CJD surveillance centres and analysed the electroencephalogram and neuroimaging data together with a panel of seven CSF biomarkers: 14-3-3, total tau, phosphorylated/total tau ratio, alpha-synuclein, neurofilament light, YKL-40, and real-time quaking induced conversion of prion protein. Using the cut-off values established for sCJD, we found the sensitivities of these biomarkers for iCJD to be similar to those described for sCJD. Given the limited relevant information on this issue to date, the present study validates the use of current sCJD biomarkers for the diagnosis of future iCJD cases.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".