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Record W1608183010 · doi:10.1186/ar1363

The use of imaging and biomarkers in the assessment and follow-up of arthritis

2004· article· en· W1608183010 on OpenAlexfundno aff
Paul Emery

Bibliographic record

VenueArthritis Research · 2004
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsnot available
FundersCanadian Arthritis NetworkSchool of Medicine, University of California, San DiegoMenzies Institute for Medical ResearchNational Cancer InstituteArthritis SocietyUniversity of North Carolina at Chapel HillGenentechNational Institutes of HealthBiogenLupus Research InstitutePfizerNatural Sciences and Engineering Research Council of CanadaDutch Arthritis AssociationOesterreichische NationalbankDeutsche ForschungsgemeinschaftNuffield FoundationPhysiotherapy Foundation of CanadaCanadian Institutes of Health ResearchNational Institute of Arthritis and Musculoskeletal and Skin DiseasesLupus Research AllianceWellcome TrustNewcastle UniversityNational Institute of Allergy and Infectious DiseasesHoward Hughes Medical InstituteAustrian Science FundArthritis Foundation
KeywordsGeography

Abstract

fetched live from OpenAlex

The Bone and Joint Monitor Project was developed to quantify the global burden of musculoskeletal conditions and develop strategies for their prevention. Experts within the Monitor Project have worked previously with officers at the World Health Organization (WHO) to estimate morbidity and mortality associated with rheumatic conditions. The present collaboration seeks means of providing additional and more current burden data. Objective To develop recommendations for performing epidemiological studies in sample populations with musculoskeletal conditions and problems, accounting for determinants and consequences to the individual and society. Methods Recommendations have been developed identifying the most relevant domains for measuring and monitoring the various musculoskeletal conditions by review of epidemiological data on occurrence, determinants and outcomes, and by expert opinion. Instruments that measure these domains were reviewed.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.844
Threshold uncertainty score0.634

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.062
GPT teacher head0.372
Teacher spread0.310 · 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

Citations0
Published2004
Admission routes1
Has abstractyes

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