Results of unicondylar knee arthroplasty
Bibliographic record
Abstract
Background. Partial knee replacement (PKR) becomes a more and more frequent method among the other methods of surgical treatment of early stages of medial knee osteoarthritis. The relevance and increasing number of PKR are confirmed by data from various national registers. The purpose of the research was to study the early functional results of PKR and to analyze the complications at various stages of the postoperative period. The assessment of the patient’s functional state according to the KSS and WOMAC was calculated as a percentage of the maximum possible sum of points for each of the scales. Material and methods. The results of 29 PKR during the period from 2016 to 2021 were analyzed. Assessment of knee function and quality of life of patients was performed according to the questionnaires Knee Scoring System (KSS), Western Ontario and McMaster Universities Arthritis Index (WOMAC), which were used preoperatively and then in 3, 6, 9, 12, 18 months after surgery. Results. The most significant improvement in quality of life and values of the functional results were observed in 3 and 18 months after surgery. After replacement it was established the best functional outcome scales of KSS (79.4%, p=0.03); WOMAC (27.1%, p = 0.02) compared with the functional results before surgery (32.3 and 73.6%, respectively). A negative correlation was revealed between body mass index and functional outcome (R = –0.7, p = 0.02). Conclusions. PKR allowed us to achieve an improvement in the quality of life and functional results already in the early postoperative period (from 3 to 18 months after the operation). The improvement of the operating technique and the analysis of errors will improve the results of PKR and minimize the number of complications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".