Osteoarthritis of the hand I: aetiology and pathogenesis, risk factors, investigation and diagnosis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".