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Record W2885334552 · doi:10.1097/rhu.0000000000000885

Transient Elastography for Monitoring for Hepatotoxicity in Rheumatoid Arthritis Patients on Long-term Methotrexate

2018· article· en· W2885334552 on OpenAlexaff
Stefanie D. Wade, Eric M. Yoshida, Mollie N. Carruthers, Michael E. Weinblatt

Bibliographic record

VenueJCR Journal of Clinical Rheumatology · 2018
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsVancouver General Hospital
Fundersnot available
KeywordsRheumatoid arthritisTransient elastographyMethotrexateMedicineTerm (time)Transient (computer programming)Internal medicineRadiologyComputer sciencePhysicsBiopsy

Abstract

fetched live from OpenAlex

JCR: Journal of Clinical Rheumatology the peer-reviewed, bimonthly journal that rheumatologists asked for. Each issue contains practical information on patient care in a clinically oriented, easy-to-read format. Our commitment is to timely, relevant coverage of the topics and issues shaping current practice. We pack each issue with original articles, case reports, reviews, brief reports, expert commentary, letters to the editor, and more. This is where you'll find the answers to tough patient management issues as well as the latest information about technological advances affecting your practice.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.050
Threshold uncertainty score0.692

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.059
GPT teacher head0.405
Teacher spread0.347 · 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 teacher head, 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

Citations5
Published2018
Admission routes1
Has abstractyes

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