Functional Outcome Study in Total Knee Arthroplasty
Bibliographic record
Abstract
Objective : The aim of this study was to validate the well-recognized outcome measure instruments (Medical Outcome Study Short Form-SF-36, Western Ontario and McMaster University Osteoarthritis Index-WOMAC, McMaster Toronto Arthritis Patient Preference Disability Questionnaire -MACTAR) for patients who had undergone total knee arthroplasty in Iran, with its cultural and ethnic differences and compare them with the reports from other parts of the world. Methods: Sixty patients, 56 women and 4 men, who had undergone total knee arthroplasty by a single surgeon, were recruited for clinical evaluation and for filling out the questionnaires on 3 outcome instrument systems, namely SF-36, WOMAC and MACTAR. Two control groups consisting of 44 cases of similar age from general population with knee discomfort and susceptible to osteoarthritis as well as 26 patients scheduled for knee arthroplasty filled out the same questionnaires. Results : The health status measurement (SF-36), disease-specific outcome measure and patient preference arthritis scores all showed significant improvement in operated cases, in both short and long term follow-up groups. Certain aspects of function like socialization with others, attending religious ceremonies and similar activities, often requiring full knee bending and/or sitting on the carpeted floor, were the main reasons for dissatisfaction with the procedure. Conclusion : The knee arthroplasty increased quality of life, improved function and produced great satisfaction in the majority of cases in our society. This is, however, a viable option for people who could change their lifestyle and household and are able to make the adjustments mentally and financially.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".