Correlation of WOMAC and KOOS scores to tibiofemoral cartilage loss on plain radiography and 3 Tesla MRI: data from the osteoarthritis initiative
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to determine the correlation between the Western Ontario and McMasters Universities Osteoarthritis Index (WOMAC) and Knee Injury Osteoarthritis Outcomes scores (KOOS) and the degree of tibiofemoral cartilage loss on plain radiography and 3T magnetic resonance imaging (MRI). We hypothesize that these subjective outcome scores will have a significant correlation to quantitative joint space loss. METHODS: Data used in the preparation of this article were obtained from the osteoarthritis initiative (OAI) database (OAI public use data sets kMRI_QCart_Eckstein18 and kXR_QJSW_Duryea16). Four hundred and forty-five patients had WOMAC/KOOS scores, quantitative tibiofemoral joints space width on plain radiographs and quantitative tibiofemoral cartilage thickness and per cent full thickness cartilage loss on 3T MRI. Joint space width on plain radiographs was correlated to cartilage thickness on MRI, and WOMAC/KOOS scores were correlated to the degree of cartilage loss using Pearson correlation coefficients. RESULTS: There was a statistically significant correlation between medial and lateral compartment cartilage thickness on MRI and medial and lateral joint space width on plain radiography (r = 0.86, r = 0.80) (p < 0.001). KOOS knee pain score was significantly correlated to increasing per cent full thickness cartilage loss in the medial femoral compartment (r = 0.34) (p < 0.001). KOOS symptom score was significantly correlated to decreasing joint space width in the medial (r = 0.16) and lateral (r = 0.15) compartment and increasing per cent full thickness cartilage loss in the medial femoral compartment (r = 0.36) (p < 0.001). No WOMAC score was correlated to degree of joint space width, cartilage thickness or per cent full thickness cartilage loss (n.s). CONCLUSION: The WOMAC and KOOS scores are poor indicators of tibiofemoral cartilage loss, with only the KOOS symptom and knee pain score being weakly correlated. Osteoarthritis is a multifactorial process and the need to treat patients based off their symptoms and rely on radiographs as confirmatory modalities, and not diagnostic modalities, when talking about OA and medical intervention.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".