Is it Possible to Recover Cardiac Functions After Total Knee Arthroplasty?
Bibliographic record
Abstract
BACKGROUND: Patients suffering from knee osteoarthritis lead a less active life than their healthy peers. It is well known that insufficient physical activity is the most common cause of chronic diseases. However, there is not enough research to enlighten the effect of increased functional capacity on cardiac functions after Total Knee Arthroplasty (TKA). This study aimed to investigate whether the orthopedic surgeons can predict that the patients will be healthier after TKA in terms of cardiac functions or not? METHODS: 109 patients who underwent TKA were prospectively followed for one year. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and short form 36 (SF-36) surveys, BMI measures, average step count per day, the six-minute walking test (6MWT), the Five-Times-Sit-to-Stand Test (FTSST) and Doppler echocardiography were performed both in the preoperative and postoperative period. RESULTS: After TKA, there was a substantial improvement in terms of WOMAC and SF36 survey scores. The average step count increased from 2199.6±690.8 steps/day to 4124.3±1638.8 steps/day. 6MWT and FTSST improved significantly as well. The average brisk walking time was 174.23±95.11 minutes/week. The means of early and late mitral inflow velocity ratios (E/A and Em/Am ratios) increased from 0.71±0.12 to 0.77±0.13 and from 0.66±0.13 to 0.76± 0.15 at the first year follow-up visit, respectively (p<0.001). CONCLUSION: echocardiography.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".