Occupational Physical Loading Tasks and Knee Osteoarthritis: A Review of the Evidence
Bibliographic record
Abstract
UNLABELLED: Purpose : To perform a systematic review with best evidence synthesis examining the literature on the relationship between occupational loading tasks and knee osteoarthritis (OA). METHODS: Two databases were searched to identify articles published between 1946 and April, 2011. Eligible studies were those that (1) included adults reporting on their employment history; (2) measured individuals' exposure to work-related activities with heavy loading in the knee joint; and (3) identified presence of knee OA (determined by X-ray), cartilage defects associated with knee OA (identified by magnetic resonance imaging), or joint replacement surgery. RESULTS: A total of 32 articles from 31 studies met the inclusion criteria. We found moderate evidence that combined heavy lifting and kneeling is a risk factor for knee OA, with odds ratios (OR) varying from 1.8 to 7.9, and limited evidence for heavy lifting (OR=1.4-7.3), kneeling (OR=1.5-6.9), stair climbing (OR=1.6-5.1), and occupational groups (OR=1.4-4.7) as risk factors. When examined by sex, moderate level evidence of knee OA was found in men; however, the evidence in women was limited. CONCLUSIONS: Further high-quality prospective studies are warranted to provide further evidence on the role of occupational loading tasks in knee OA, particularly in women.
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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".