Rating Systems to Assess the Outcomes After Total Knee Arthroplasty.
Bibliographic record
Abstract
INTRODUCTION: To assess the success of a total knee arthroplasty (TKA), scoring systems have been developed to provide a straightforward method of evaluating the outcomes of patients following surgery. Fully evaluating these outcomes is a challenging and time consuming task, and these simplistic measures often do not provide a complete picture of a patient's recovery. Therefore, we evaluated different scoring systems to determine the most effective method of assessing the outcomes of patients undergoing total knee arthroplasty. MATERIALS AND METHODS: We evaluated all knee scoring systems currently available in literature, and a total of 46 questionnaires met our inclusion and exclusion criteria. We then identified all the metrics assessed in the questionnaires (n=48) and subdivided them into objective, subjective, rehabilitative, and quality of life outcome measures. We identified the three most commonly referenced questionnaires (the Knee Society Scores, the Knee Osteoarthritis and Outcomes Scores, and the Western Ontario and McMaster Score-WOMAC) and assessed multiple permutations of these with other scoring systems to identify the combinations that would most comprehensively and efficiently evaluate the outcomes of patients undergoing TKA. RESULTS: Of the 48 metrics, we identified four subjective, eight objective, 20 rehabilitation, and 16 quality of life metrics. On permutation of the three most referenced scoring systems, the KSS and the KOOS together yielded the greatest coverage of the above metrics (79%). When the KSS, KOOS, and WOMAC, respectively, were combined with the Lower Extremity Function Scale (LEFS) and Short Form 36 (SF-36), they yielded 77, 73, and 60% coverage of the metrics and 35, 39, and 37% redundancy, respectively. CONCLUSION: Surgeons and researchers have attempted to fully evaluate the outcomes of patients undergoing TKA. The proposed combinations may provide a more comprehensive way to cost-effectively evaluate outcomes. Further analysis is required before attempting to create newer knee scoring systems.
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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 | 0.020 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".