Comparison of KOOS Scores of Middle-Aged Patients Undergoing Total Knee Arthroplasty to the General Dutch Population Using KOOS Percentile Curves: The LOAS Study
Bibliographic record
Abstract
BACKGROUND: We aimed to investigate the application of the Knee Injury and Osteoarthritis Outcome Score (KOOS) percentile curves, using preoperative and postoperative data of patients with knee osteoarthritis undergoing total knee arthroplasty (TKA). METHODS: We used Longitudinal Leiden Orthopedics Outcomes of Osteo-Arthritis study data of patients between 45 and 65 years and undergoing primary TKA. KOOS scores (0-100) were obtained preoperatively and 6, 12, and 24 months after TKA. Preoperative knee radiographs were assessed according to Kellgren-Lawrence (KL) in a subset (37%) of patients. Comorbidities were self-reported using a standardized questionnaire. The median (interquartile range) population-level KOOS scores were plotted on previously developed population-based KOOS percentile curves. In addition, we assessed the application of the curves on patient level and investigated differences in scores between patients with preoperative KL scores ≤2 and ≥3 and presence (vs absence) of comorbidities. RESULTS: percentile 12 months postoperatively. Greater improvements were observed in pain and less improvements in sport and recreational function and quality of life. Patients with higher preoperative KL scores and without comorbidities showed greater improvements. CONCLUSION: The KOOS percentile curves provided visual insights in knee complaints of patients relative to the general population. Furthermore, the KOOS percentile curves give insight in how preoperative patient characteristics are correlated with postoperative results.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".