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Record W1601511168 · doi:10.1186/ar1348

An apoptotic signaling pathway activated by Nod1

2004· article· en· W1601511168 on OpenAlexfundno aff
Jean da Silva Correia, Yvonne Miranda, Nikki Austin-Brown, John C. Mathison, Jullet Han, R. J. Ulevitch

Bibliographic record

VenueArthritis Research · 2004
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Response and Inflammation
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
KeywordsBusiness

Abstract

fetched live from OpenAlex

IntroductionThe 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. ResultsThe domains recommended follow the principles of the WHO International Classification of Functioning, Disability and Health [1,2], and consider: health condition; body function and structure; activity limitation; participation restriction; personal and environmental contextual factors; and, in addition, the resource utilisation and social consequences.The musculoskeletal conditions and problems considered were osteoarthitis, inflammatory arthritis, osteoporosis, spinal problems, musculoskeletal trauma and injuries, and musculoskeletal pain with restricted activity.The selection of indicators for each domain considered the feasibility of their use in a health interview survey (HIS), a health examination survey (HES), a register or a clinical study.Consensus on case definition was reached depending on the study methodology.For example, osteoporosis defined by bone densitometry cannot be ascertained in an HIS, whereas the outcome of osteoporosis (i.e.fragility fracture) can be.Osteoarthitis can be identified as joint pain in an HIS but the preferred definition is pain with X-ray changes and can only be ascertained in an HES.Previously validated generic and disease-specific instruments have been identified that include indicators for all or most of the recommended domains for the consequences of the different conditions and problems.The indicators of the domains for resource utilisation and social consequences and feasibility for col-lection will vary in different socioeconomic and geographic areas.Guidance on sampling methods is also being developed.Conclusions The comparability of data collected across the globe will improve by the application of agreed upon indicators that consider key domains for the different musculoskeletal conditions and problems in epidemiological studies conducted in different populations.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.003

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 head0.026
GPT teacher head0.312
Teacher spread0.285 · 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.

Study designBench or experimental
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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