Knee osteoarthritis in Iranian women: A cross-sectional study from Yassoj, Iran
Bibliographic record
Abstract
Abstract Background: One of the most prevalent musculoskeletal diseases (MSDs) is knee osteoarthritis (KO), which results in patients experiencing significant pain, decreased function and range of motion, lower income, decreased social contact, and ultimately diminished quality of life. This study sought to evaluate the risk factors for knee osteoarthritis in women between the ages of 40 and 55 because several studies have shown that KO is frequent among these females. Methods: One hundred of the 198 individual who were randomly chosen for this descriptive-analytical study were eligible and included because they met the inclusion criteria. The amount of protective behavior was assessed using a 38-item self-design checklist. Knee function and pain intensity were assessed using the Western Ontario and McMaster Universities Arthritis Index (WOMAC) and the visual analog scale (VAS). In addition, a muscle strength assessment and a self-efficacy questionnaire were applied. Using SPSS version 24, analytical tests were run on the collected data. Results: This study showed there is a significant relationship between knee osteoarthritis and various factors such as age, body mass index, educational status, other diseases (blood pressure and diabetes), muscle strength, daily housewives’ performance, knee-protective behaviors, and self-efficacy in the field of knee-protective behaviors (P < 0.05 in all instances). Conclusion: Providing educational and training programs for women to prevent knee osteoarthritis is highly advised since, as this study's findings showed, the majority of risk factors linked to knee discomfort from osteoarthritis can be managed.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".