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Record W2064608069 · doi:10.1002/sim.4126

Assessment of joint symmetry in arthritis

2011· article· en· W2064608069 on OpenAlexfundaboutno aff
Lynne Cresswell, Vernon T. Farewell

Bibliographic record

VenueStatistics in Medicine · 2011
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsnot available
FundersMedical Research CouncilUniversity of Toronto
KeywordsJoint (building)Symmetry (geometry)Psoriatic arthritisArthritisConsistency (knowledge bases)Joint diseaseContingency tableDiseaseMedicineTable (database)Goodness of fitEconometricsMathematicsComputer scienceStatisticsArtificial intelligenceData miningPathologyInternal medicineAlternative medicineStructural engineering

Abstract

fetched live from OpenAlex

We evaluate three methods for the assessment of symmetry in the joints affected by an arthritic disease. The first two methods, based on published methodology, are limited by their assumptions. We introduce a third method that enables a more comprehensive investigation. In common with previous methods, this method examines tabulations of observed data for evidence of symmetry. Expected values for the table cells are simulated under an assumption of independent joint disease, whilst allowing for differences between patients, and joint locations, in terms of their susceptibility to disease symptoms. Departures of observed from expected values are assessed via a Pearson-type goodness-of-fit test and are examined for consistency with symmetry. We illustrate the three methods using data on the damage accrued in the hand joints of patients registered at the University of Toronto Psoriatic Arthritis clinic.

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)0.038
metaresearch head score (Gemma)0.160
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.200

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.160
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0080.004
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.041
GPT teacher head0.349
Teacher spread0.308 · 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

Citations4
Published2011
Admission routes2
Has abstractyes

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