Bibliographic record
Abstract
Total knee replacement (TKR) is the mainstay of treatment for people with end-stage knee OA among suitably "fit" candidates. As a high cost, high volume procedure with a worldwide demand that continues to grow it has become increasingly popular to measure response to surgery. While the majority who undergo TKR report improvements in pain and function following surgery, a significant proportion of patients report dissatisfaction with surgery as a result of ongoing pain or poor function. Poor outcomes of TKR require care that imposes on already overburdened health systems. Accurate and meaningful capture and interpretation of outcome data are imperative for appropriate patient selection, informing those at risk, and for developing strategies to mitigate the risk of poor results and dissatisfaction. The ways in which TKR outcomes are captured and analysed, the level of follow-up, the types of outcome measures used, and the timing of their application vary considerably within the literature. With this in mind, we reviewed four of the most commonly used joint specific outcome measures in TKR. We report on the utility, strengths, and limitations of the Oxford knee score (OKS), knee injury and osteoarthritis outcome score (KOOS), Western Ontario and McMaster Universities osteoarthritis index (WOMAC), and knee society clinical rating system (KSS).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".