Residual knee pain and functional outcome following total knee arthroplasty in osteoarthritic patients
Bibliographic record
Abstract
PURPOSE: Total knee arthroplasty (TKA) is a successful and safe elective operation in managing patients with severe osteoarthritis of the knee. However, the presence of residual knee pain (RKP) post-TKA can adversely affect patient satisfaction and functional outcome. Hence, the aim of this paper is to identify the incidence, progression of knee pain, functional outcome post-TKA and possible predictive factors for the development of RKP post-TKA. METHODS: A retrospective review of 357 patients was conducted with a minimum follow-up period of 2 years. Predictive factors reviewed include the patients' demographics, co-morbidities, type of implants and patellar management. For functional outcome, the patient's Knee Society Score (KSS) and Western Ontario and McMaster Universities Index of Osteoarthritis scores were analysed. To determine presence and severity of RKP, the sub-score for pain in the KSS was utilised. RESULTS: In total, 31.1 and 28.9% of the patients were found to have RKP at 1 and 2 years, respectively, though their functional outcome scores continued improving from 1 year. Ischaemic heart disease (IHD) patients were more likely to have RKP and poorer functional outcome at 1 year. Males and patients with posterior-stabilised implants were found to have better functional outcome at 1 and 2 years, respectively. CONCLUSIONS: Almost a third of the patients continued to have RKP at 2 years post-TKA, with factors such as gender, presence of IHD and implant type significantly associated with the development of RKP and/or poorer functional outcome scores. By recognising the incidence and predictive factors for RKP, physicians will be able to better manage their patients' expectations and optimise their pre-morbid status pre-operatively. LEVEL OF EVIDENCE: III.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".