Aetiology of Total Knee Arthroplasty (TKA) Failure and Assess the Functional Outcome of Patients Who Underwent Revision Total Knee Arthroplasty
Bibliographic record
Abstract
Objective: To identify the causes of total knee arthroplasty (TKA) failure and assess the functional results of patients who received revision TKA. Study Design: Descriptive Study Place and Duration: Orthopedic department of Khyber Teaching Hospital Peshawar in the duration from 1st June, 2022 to 30 November, 2022 Methods: The study included all patients who had undergone initial TKA and subsequently underwent revision TKA. Functional outcomes following revision TKA were assessed at six months using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) questionnaire, and the revision arthroplasty criteria were validated. Statistical significance was determined using a t-test that compared patients' WOMAC scores before and after surgery. Results: A total of 312 revision TKAs were performed on 200 patients in the study, with 89.6% of them transferred to our center. Sixty-nine percent of patients who underwent TKA required a further revision procedure. Over four fifths (73.7%) of all revisions were made after the event. 122 (38.1%) of the cases had been resolved in some fashion. The most common reason for a second surgery after an initial replacement was infection (36.1%), followed by aseptic loosening (21.9%) and periprosthetic fracture (13.7%). The majority of our patients who underwent a second arthroplasty were happy with the functional outcomes. Conclusion: The three most common reasons for a TKA to fail are infection, periprosthetic fracture, and aseptic loosening. Significant improvements in functional outcomes were seen with revision TKA, albeit a sizable proportion of patients still suffered or required additional intervention. Keywords: TKA, Infection, Revision of surgery
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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".