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Chronic Cerebrospinal Venous Insufficiency and Multiple Sclerosis: Changes in Treatment Patterns and Opinions in a Province-Wide Study (P05.183)

2013· article· en· W8843616 on OpenAlexaff
Luanne M. Metz, Ruth Ann Marrie, Katayoun Alikhani, Gregg Blevins, Jacqueline Bakker, Nathalie Jetté, Oksana Suchowersky, Mary Louise Myles, Winona Wall, James F. Newsome, Jamie Greenfield, Marcus Koch, Scott B. Patten, Scott Kraft, Derek Emery, Mayank Goyal

Bibliographic record

VenueNeurology · 2013
Typearticle
Languageen
FieldMedicine
TopicCerebral Venous Sinus Thrombosis
Canadian institutionsFoothills Medical CentreUniversity of ManitobaAlberta HealthUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsMedicineMultiple sclerosisObservational studyPopulationPediatricsPhysical therapyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: We aimed to describe the population of Multiple Sclerosis (MS) patients reporting chronic cerebrospinal venous insufficiency (CCSVI) treatment and to describe the change in treatment patterns and opinions over time.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.126
Threshold uncertainty score0.251

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.036
GPT teacher head0.256
Teacher spread0.220 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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