Predicting the Outcome of Total Knee Arthroplasty Using the WOMAC Score: A Review of the Literature
Bibliographic record
Abstract
It is estimated that up to a third of recipients of total knee arthroplasty (TKA) experience chronic pain postoperatively. However, there are no clear indications within the literature that predict which patients are at higher risk of being dissatisfied with their TKA. The Western Ontario and McMaster University Osteoarthritis Index (WOMAC) is one of the most commonly used, patient-reported outcome measures in patients with lower limb osteoarthritis. This review discusses the available evidence surrounding the predictability of the outcome of TKA using the WOMAC score as well as considering further patient factors that have been implicated in the level of improvement post TKA. It may be concluded from the available literature that a combination of knee scores and patient factors would be the most accurate way of predicting those patients most likely to have a good outcome from their TKA. There is some disparity within the literature about which patient factors and reported outcome measure scores lead to a positive postoperative outcome. Patient expectations following the procedure also need to be evaluated, as objective measures on a scoring system do not necessarily equate with the subjective patient experience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".