Evaluating Short-Term Patient-Reported Outcome Measures Following Total Hip and Knee Replacement: A Comprehensive Review
Bibliographic record
Abstract
Total hip replacement (THR) and total knee replacement (TKR) are widely performed surgical procedures to alleviate pain and improve function in patients with joint-related diseases. Short-term patient-reported outcome measures (PROMs) have become a key metric in assessing the success of these surgeries from the patient's perspective, focusing on early recovery, pain management, mobility, and quality of life. This comprehensive review evaluates the significance of short-term PROMs following THR and TKR, highlighting commonly used tools such as the Oxford Hip Score (OHS), Oxford Knee Score (OKS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and short-form health survey (SF-36). The analysis explores the impact of various factors, such as age, preoperative health status, and surgical technique, on short-term outcomes. Findings from recent studies indicate that while patients generally report improvements in physical function and pain relief within the first six months post-surgery, individual outcomes can vary significantly. Factors like early rehabilitation, mental health, and the presence of postoperative complications can influence the trajectory of recovery and satisfaction levels. Moreover, the review addresses the limitations of current PROMs, including variability in reporting and sensitivity to different patient populations. This review emphasizes the need for more personalized and standardized approaches to PROM assessment to better capture patient experiences and optimize postoperative care. Future research should focus on integrating PROMs with long-term follow-up data and digital health tools to track real-time patient progress, thus enhancing the overall quality of care for THR and TKR patients. Short-term PROMs play a vital role in understanding patient outcomes and guiding clinical practice for joint replacement surgeries.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".