Knee Osteoarthritis Prevalence in Hospitalized Elderly Patients: A Retrospective Study
Bibliographic record
Abstract
This study aimed to determine the prevalence rate of knee osteoarthritis (OA) and the risk factors for OA in hospitalized elderly patients. We conducted this retrospective study in elderly patients (aged 65 years and older) who were hospitalized in the Geriatric Ward of General Hospital of Guangzhou Military Command of the People's Liberation Army between January 2011 and June 2013, including general condition, present history, past history, physical examination, X-ray results, and disease diagnosis. The prevalence, awareness, and treatment rates of knee OA in hospitalized elderly patients were calculated. Risk factors were computed using multiple logistic regression analysis. Of a total of 267 (17.4%) hospitalized elderly patients diagnosed with knee OA, the prevalence rate of OA was 9.95% in males and 37.76% in females. The rate of awareness among those with OA was 51.68%; the rate of treatment was 83.33%; and the rate of control was 77.39%. The medical expenses for both females (1143±315 yuan month-1) and males (1192±357 yuan month-1) in knee OA patients are higher than that of the non-knee OA group (989±274 yuan month-1, 1038±295 yuan month-1). The risk factors for knee OA include gender (OR=2.448), age (OR=1.124), transportation mode (OR= 8.972), exercise (OR=7.374), bowel evacuation position (OR=5.767), family history of knee OA (OR=2.195), and body mass index (OR=2.469). The prevalence of knee OA is unexpectedly high in hospitalized elderly patients, and the rates of awareness and treatment are less than desirable. Prevention and control measures should be taken in patients with concomitant risk factors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".