Exploration of combined factors related to quality of life after knee replacement surgery
Bibliographic record
Abstract
In this study, we aimed to identify combined factors associated with lower postoperative quality of life (QOL) in knee replacement (KR) patients, utilising data from the Osteoarthritis Initiative (OAI) database. The data of 44 individuals from the OAI who underwent KR surgery was included in this study. Preoperative baseline data, including demographic information, comorbidities, depressive symptoms, knee-joint symptoms, and health-related QOL, were analysed using association rule analysis to identify single and combined factors linked to low postoperative QOL that were assessed with Short Form-12. Preoperative factors such as comorbidities, high Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC)-pain scores, poor physical function, and older age were strongly associated with lower postoperative physical component scores (PCS) at 2 years. When combined, these factors showed even stronger associations with lower PCS. No significant associations were found with PCS and mental component scores (MCS) at 1 and 2 years postoperatively. Our findings emphasize the importance of evaluating combined preoperative factors, including comorbidities, pain levels, physical function, and age, as they may be associated with lower postoperative QOL in patients who underwent KR. Considering combined factors, rather than assessing single factors in isolation, may provide a more appropriate understanding of postoperative outcomes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".