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Record W2953437325 · doi:10.1017/cjn.2019.72

The Predictive Value of Endpoint Quaking-Induced Conversion in Creutzfeldt-Jakob Disease

2019· article· en· W2953437325 on OpenAlexafffundvenueabout
Adrian Budhram, Ryan Taylor, Jeff Fuller, Jorge G. Burneo, J. David Knox, Stephen Pasternak

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPrion Diseases and Protein Misfolding
Canadian institutionsPublic Health Agency of CanadaWestern University
FundersEpilepsy Research Program of the Ontario Brain InstituteOntario Brain Institute
KeywordsPredictive valueMedicineDiseaseVirologyPathologyInternal medicine

Abstract

fetched live from OpenAlex

Creutzfeldt-Jakob disease (CJD) is a fatal neurological illness for which accurate diagnosis is paramount. Real-time quaking-induced conversion (RT-QuIC) is a prion-specific assay with high sensitivity and specificity for CJD. The Canadian endpoint quaking-induced conversion (EP-QuIC) test is similar, but unlike RT-QuIC there is little data regarding its diagnostic utility in clinical practice. In this exploratory predictive value analysis of EP-QuIC in CJD, the negative predictive value (NPV) and positive predictive value (PPV) was 100% and 83%, respectively, with one false-positive result identified. Re-testing this sample with an optimized EP-QuIC protocol eliminated this false-positive result, leading to a PPV of 100%.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.016
GPT teacher head0.255
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2019
Admission routes4
Has abstractyes

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