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Record W2162942927 · doi:10.1111/jphp.12196

Osteoarthritis of the hand I: aetiology and pathogenesis, risk factors, investigation and diagnosis

2013· review· en· W2162942927 on OpenAlexaff
Garvin J. Leung, K. D. Rainsford, Walter F. Kean

Bibliographic record

VenueJournal of Pharmacy and Pharmacology · 2013
Typereview
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsMcMaster UniversityHamilton Health Sciences
Fundersnot available
KeywordsEtiologyOsteoarthritisPathogenesisMedicineMedical diagnosisObesityRisk factorBioinformaticsPsychological interventionPhysical therapyIntensive care medicinePathologyBiologyAlternative medicinePsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: Osteoarthritis (OA) of the hand can be a debilitating condition that hinders an individual's quality of life. With multiple joints within the hand that are commonly affected OA, an individual's ability to use their hand in everyday movements become more limited. The article aims to review literature on the aetiology and pathogenesis of OA, risk factors, characteristics of hand OA and the steps of diagnosis. KEY FINDINGS: The aetiology and pathogenesis of OA, in particular hand OA, is not fully understood. However, it is known that several factors play a role. Environmental factors, such as stress from mechanical loading, especially to vulnerable joints predispose individuals to developing OA. Extracellular matrix changes in protein levels have also been noted in individuals with OA. Linked to hand OA development are boney enlargements (Herbeden's and Bouchard's nodes). Several risk factors for OA include: age, obesity, gender, smoking, genetics, diet and occupation. Various diagnostic methods include a combination of using radiographic methods, clinical presentation, a number of developed measurements and scales. SUMMARY: With OA having several risk factors and various causes and contributing elements, it is important to elucidate the pathogenesis of OA and determine exactly how risk factors play a role in its development. Because of the contributions from several elements, diagnosis is best when it uses multiple methods. In turn, understanding OA and making better diagnoses could lead to improved management of the condition through both pharmacological and non-pharmacological interventions.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.973
Threshold uncertainty score0.820

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.049
GPT teacher head0.339
Teacher spread0.290 · 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 designOther design
Domainnot available
GenreReview

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

Citations97
Published2013
Admission routes1
Has abstractyes

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