Association Between Preoperative Radiographic Severity of Osteoarthritis and Patient-Reported Outcomes of Total Knee Replacement
Bibliographic record
Abstract
BACKGROUND: The goal of this study was to investigate the association between preoperative radiographic severity of knee osteoarthritis (OA) and patient-reported outcomes following total knee replacement. METHODS: We used data from a prospective cohort study of individuals who underwent total knee replacement at a high-volume medical center. Patient-reported outcomes included the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain score and the Knee injury and Osteoarthritis Outcome Score (KOOS) activities of daily living (ADL) subscore, assessed preoperatively and 2 years postoperatively. We measured preoperative radiographic OA severity using the Osteoarthritis Research Society International (OARSI) Atlas score, dichotomized at the median. We assessed the association between radiographic OA severity and postoperative patient-reported outcomes in bivariate analyses and in multivariable linear regression, with adjustment for age, sex, body mass index, and comorbidity score. RESULTS: The analytic cohort included 240 patients with a mean age at surgery of 66.6 years (standard deviation, 8 years); 61% were female. The median total OARSI radiographic severity score was 10 (range, 3 to 17). The cohort improved substantially at 2 years following total knee replacement, with WOMAC pain and KOOS ADL score improvements on the order of 30 points. We did not observe significant or clinically important differences in pain relief or functional improvement between patients with milder and more severe radiographic OA. Sensitivity analyses using other radiographic assessment measures yielded similar findings. CONCLUSIONS: Total knee replacement offers substantial symptomatic relief and functional improvement regardless of preoperative radiographic OA severity. LEVEL OF EVIDENCE: Prognostic Level IV. See Instructions for Authors for a complete description of levels of evidence.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".