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Record W2886031194 · doi:10.1017/cjn.2018.74

The Positive Predictive Value of Onconeural Antibody Testing: A Retrospective Review

2018· review· en· W2886031194 on OpenAlex
Adrian Budhram, Michael Nicolle, Liju Yang

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2018
Typereview
Languageen
FieldMedicine
TopicAutoimmune Neurological Disorders and Treatments
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsMedicineMalignancyCancerPredictive valueImmunotherapyInternal medicine

Abstract

fetched live from OpenAlex

Paraneoplastic syndromes (PNS) are immune-mediated neurologic diseases that occur as an indirect effect of malignancy, and can be challenging to diagnose. Onconeural antibodies have a greater than 95% association with cancer, and their presence in a patient with neurologic symptoms is reportedly highly indicative of PNS. However, we performed a single-centre retrospective review to determine the positive predictive value of onconeural antibody testing, and found it to be concerningly low (39%). Recognising the limitations of onconeural antibody testing is critical to ensure accurate test interpretation, avoid unnecessary repeated malignancy screening and prevent the use of potentially hazardous immunotherapy.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.511
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.003
Science and technology studies0.0030.017
Scholarly communication0.0000.000
Open science0.0030.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.060
GPT teacher head0.350
Teacher spread0.290 · 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