Demographic, pain, and quality of life factors in unilateral and bilateral total knee arthroplasty: a population-based retrospective cohort study of 23,033 patients
Bibliographic record
Abstract
Abstract Background The Alberta Bone and Joint Health Institute (ABJHI) has a comprehensive registry capturing clinical, demographic, and patient-reported outcome measures on all knee arthroplasties performed in the province of Alberta, Canada. This large database provides an opportunity to study the outcomes of total knee arthroplasties (TKA) in patients who underwent unilateral or bilateral procedures. Methods Pain and quality of life outcomes of 29,157 TKAs performed in 23,033 patients for knee osteoarthritis performed in Alberta, Canada from January 2013 to February 2020 were examined. This study: (1) investigates whether having both knees replaced results in better pain and quality of life outcomes than replacing only one knee (using multivariate analysis of variance) and (2) identifies if pain and quality of life outcomes impact the likelihood of needing surgery in the contralateral knee after a first TKA (using univariate and multivariate Cox proportional hazards models). Patient-reported outcomes were measured using questionnaires: the Western Ontario and McMaster Universities Arthritis Index (WOMAC) to assess knee pain, and the EuroQol-5D-5L (EQ-5D) to assess quality of life. Results Patients who had surgery on both knees had approximately 5% better WOMAC (pain) and EQ-5D (quality of life) questionnaire scores 12 months post-surgery than those who had surgery on only one knee. Additionally, for every 1-point higher pre-operative WOMAC pain score (i.e., less pain) there was a 1% decreased likelihood of undergoing a contralateral TKA compared to a 46% decreased likelihood per 1-point improvement in quality of life. Dementia, back pain, and hospital readmission were associated with a 68%, 17%, and 44% decreased likelihood of contralateral TKA respectively. By contrast, patients with obesity were 36% more likely to undergo bilateral TKA (p < 0.05). Conclusions Preoperative knee pain and quality of life play a statistically significant role in determining which patients undergo bilateral TKA. Additionally, bilateral TKA resulted in better patient-reported outcomes compared to unilateral TKA. Though clinical significance cannot be drawn from the low response rates for the patient-reported outcomes, these findings provide a basis for future research on patient-reported outcomes within the Canadian healthcare system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".