Radiographic severity of knee osteoarthritis and its relationship to outcome post total knee arthroplasty: a systematic review
Bibliographic record
Abstract
BACKGROUND: Up to 20% of patients are dissatisfied after total knee arthroplasty (TKA). There are many contributing factors. The relationship between preoperative osteoarthritis (OA) severity and outcome post TKA remains unclear. This review explores the relationship between preoperative OA severity with patient reported pain, function and satisfaction post TKA. METHODS: A pre-registered systematic review was performed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Major databases were searched until September 2017. We included studies assessing adults undergoing TKA for OA. Minimum follow-up was 6 months. Methodological quality assessment was conducted using the Newcastle-Ottawa Scale. RESULTS: Twenty cohort studies with 7478 patients were included. There were 16 good, one fair and three poor quality studies. Knee OA was most commonly reported according to the Kellgren and Lawrence tool. Ten studies showed statistically significant pain outcomes for those with worse preoperative OA. This was supported by meta-analysis of the Knee Society Score pain change scores to final follow-up for those with Kellgren and Lawrence grade 4 OA. Six studies showed statistically significant results for various aspect of functional recovery, although meta-analysis of Knee Society Score function change scores identified no difference. Meta-analysis of final follow-up pain and function scores alone yielded no significant difference. Patients with more severe preoperative OA were more likely to be satisfied. There were no studies demonstrating that less severe OA resulted in better pain, function or satisfaction. CONCLUSION: Review of available research indicates that TKA for OA improves pain, function and satisfaction. Those with more severe preoperative radiological knee OA benefit most.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".