Disabling knee pain – another consequence of obesity: Results from a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Obesity is linked to knee osteoarthritis (OA) and knee pain. These are disabling problems that are more prevalent in older adults. No prospective study has estimated the impact of excess weight avoidance on the occurrence of knee pain in the general older population. The aim of this study was to investigate the influence of overweight and obesity on the onset and progression of knee pain and disability in older adults living in the community. METHODS: A prospective cohort study of people aged 50 and over registered with three general practices in North Staffordshire, UK. 5784 people who had responded to a survey in March 2000 were mailed a follow-up questionnaire in March 2003. The main outcome measures were self-reported knee pain and severe knee pain and disability at 3 years measured by the Western Ontario and McMaster Universities Osteoarthritis index. RESULTS: Adjusted response to follow-up was 75%. Among responders with no knee pain at baseline, obesity predicted onset of severe knee pain (relative risk 2.8; 95% CI 1.8, 4.5 compared to normal body mass index (BMI) category). Considering overweight and obese categories together, 19% of new cases of severe knee pain over a 3-year period could potentially be avoided by a one-category shift downwards in BMI; this includes almost half of the new cases that arose in the obese group. CONCLUSION: Obesity accounts for a substantial proportion of severe disabling knee pain. As knee pain is a common disabling condition in older adults living in the community, effective public health interventions about avoidance of excess weight could have a major impact on future lower limb disability in older adults.
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 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".