Bibliographic record
Abstract
Osteoarthritis (OA) is a multifactorial disease of the joints with a complex interplay between systemic factors, such as age, sex, genetic components, obesity and environmental factors (including smoking, diet, physical activity, joint injury and muscle function). Among those risk factors, genetic and modifiable factors (obesity) have been shown to have a crucial role in the development and progression of the disease on radiographs; however, how genetic factors and obesity influence the progression of early structures on magnetic resonance imaging (MRI) and its symptoms (pain) is not fully understood. This thesis aims to explore how these two factors separately or interactively are associated with important structural outcomes on MRI and pain. Data from two longitudinal studies were utilised (the Offspring and TASOAC study). In the offspring study, 372 individuals (186 offspring having at least one parent with a total knee replacement (TKR) for severe primary knee OA and 186 controls) aged 26‚Äö-61 years (mean age of 45 years) participated at baseline and were followed 2.3 and 10.2 years later. TASOAC study is a population-based study with 1099 older adults aged 50-80 years (mean age of 62 years) enrolled at baseline and followed approximately 2.6 and 5.1 years. Cartilage volume, cartilage defects, bone marrow lesions (BMLs), meniscal pathology and effusion were assessed by MRI.Radiographic OA was assessed by X-ray. Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) was used to assess knee pain. A self-reported questionnaire was used to assess pain at neck, back, hands, shoulders, hips, knees and feet. Fat mass was assessed using dual energy x-ray absorptiometry. Data from the Offspring study was used to describe the associations of family history of knee OA with worsening knee pain and knee structural changes over 10 years. We found that offspring had an increased risk of worsening knee pain as compared to controls with no family history of knee OA, and this association was independent of structural factors. Also, offspring had an increased risk of worsening multiple knee structural abnormalities including cartilage defects, meniscal extrusion and tears but not BMLs. The associations between weight and knee cartilage volume/defects over 10 years in offspring and in controls were also examined from the same population. Increasing body weight was deleteriously associated with medial tibiofemoral cartilage volume and presence of medial tibiofemoral cartilage defects in offspring. Similar associations were observed for lateral tibiofemoral cartilage volume and defects. However, there were no statistically significant associations between weight and cartilage volume or defects in controls. The fourth study utilised data from the TASOAC study to explore the associations of fat mass, fat mass index (FMI) and body mass index (BMI) with multi-site pain (MSP), finding that fat mass was associated with MSP and pain at the hands, knees, hips and feet. Results were similar for FMI and BMI. The final study, in the same population, found that the presence of MSP independently predicts knee cartilage volume loss. In conclusion, this series of studies suggest that both genetic and systemic factors (especially fat mass) may have an important role in early structural changes and pain in OA, and these two factors interact with each other to involve in the pathogenesis of OA.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".