PREDICTION OF FEAR FACTORS BEFORE TOTAL KNEE REPLACEMENT: A MIXED METHOD ANALYSIS ON ADVANCED KNEE OSTEOARTHRITIS PATIENTS
Bibliographic record
Abstract
Objective: To determine fear factors before total knee replacement in advanced knee osteoarthritis patients. Study Design: Mixed method study. Place and Duration of Study: Study was conducted at department of Orthopedics, Pakistan Ordinance Factory Hospital Wah Cantt, form May 2017 to May 2019. Methodology: A sample of 105 knees was calculated using WHO calculator. Patients of Knee osteoarthritis were selected with consecutive sampling. Interviews were taken from patients for determining fear factors related to total knee replacement. SPSS version 24 was used for analysis. Results: Total 105 knees of osteoarthritis were included in study. There were 51 (48.6%) male and 54 (51.4%) female in study. Mean age of patients was 59.8 ± 4.1 SD years. Most common fear factor according to patients priority for total knee replacement therapy was worsening of symptoms after total knee replacement 42 (40%) followed by pain persistence after total knee replacement 27 (25.7%), financial feasibility 21 (20%), and religious problems 15 (14.3%). Patients with osteoarthritis grading 4 had high pain scores, high stiffness scores and lower social function scores (p=0.000, p=0.000, p=0.001 respectively) on Western Ontario and McMaster Universities Osteoarthritis Index scale. Conclusion: Total knee replacement therapy in advanced knee osteoarthritis is associated with several fear factors. Most common fear factor is worsening of symptoms after total knee replacement followed by pain persistence. Total knee replacement efficacy related counseling and community based awareness programs could be helpful in overcoming these fears and stigmas in Pakistan.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| 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) | 0.003 | 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".