Diverse rehabilitation measures applied for restorative treatment of total hip arthroplasty patients (own findings and literature review)
Bibliographic record
Abstract
IIntroduction Total hip arthroplasty (THA) is one of the most successful orthopedic procedures performed today.Rates of THA have been steadily increasing over the past several decades with increasing number of patients who need proper effective rehabilitation therapy after orthopaedic surgery.Evaluation and introduction of new rehabilitation techniques is crucial for patients undergoing replacement of major joints.Objective Review the literature and our own findings with various rehabilitation programs used for THA patients to aid recovery following surgery at a short and long term.Material and methods The study included 57 THA patients referred to rehabilitation department of the Kurgan Ilizarov Center to help manage pain at different terms following surgery.The sample was divided into main (n = 29) and control (n = 28) groups.Post-isometric relaxation techniques were included in rehabilitation program of the main group.Clinical outcomes were evaluated with VAS, the Lequesne Index, McGill Pain Questionnaire, WOMAC, and Harris Hip Score.Results Outcome measures showed 1.5 times improvement in controls with high statistical significance (p > 0.01) and 3.3 times improvement in patients who received post-isometric relaxation therapy with greater significance level (p > 0.001).Conclusion The findings suggest that post-isometric relaxation techniques applied as a part of restorative treatment facilitate improved outcomes of rehabilitation.The optimal rehabilitation protocols have been shown to be largely unknown for THA patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".