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Record W2165332393 · doi:10.1016/s0140-6736(13)61747-x

Prevalence of extracranial venous narrowing on catheter venography in people with multiple sclerosis, their siblings, and unrelated healthy controls: a blinded, case-control study

2013· article· en· W2165332393 on OpenAlexafffund
Anthony Traboulsee, Katherine Knox, Lindsay Machan, Yinshan Zhao, Irene M. Yee, Alexander Rauscher, Darren Klass, Peter Szkup, Robert Otani, David Kopriva, Shanti Lala, David K. Li, A. Dessa Sadovnick

Bibliographic record

VenueThe Lancet · 2013
Typearticle
Languageen
FieldMedicine
TopicCerebral Venous Sinus Thrombosis
Canadian institutionsUniversity of SaskatchewanUniversity of British ColumbiaUniversity of British Columbia Hospital
FundersVancouver Coastal Health Research InstituteCanada Research ChairsMultiple Sclerosis SocietyLotte and John Hecht Memorial FoundationMultiple Sclerosis Society of CanadaSaskatoon City Hospital FoundationUniversity of Saskatchewan
KeywordsMedicineMultiple sclerosisVenographyCatheterCerebrospinal fluidVeinInternal jugular veinInternal medicineSurgeryThrombosisImmunology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.039
GPT teacher head0.265
Teacher spread0.226 · 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

Citations96
Published2013
Admission routes2
Has abstractno

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