Total Knee Replacement as a Knee Osteoarthritis Outcome
Bibliographic record
Abstract
OBJECTIVE: To predict, using clinical and qMRI data, the incidence of total knee replacement (TKR) during the long-term follow-up of knee osteoarthritis (OA) patients who formerly received chondroitin sulfate (CS) or placebo treatment. DESIGN: A post hoc intention-to-treat analysis to evaluate the incidence of TKR was done on knee OA patients who had participated in a 12-month trial evaluating the impact of CS (800 mg/d) versus placebo for 6 months, followed by a 6-month open-phase in which all patients received CS. Additionally, the clinical and qMRI predictors of TKR were determined. RESULTS: Thirteen TKRs were performed in the population after a 4-year follow-up. More TKRs were performed in the placebo group than in the CS group (69% vs. 31%, P = 0.150, logistic regression). The statistically significant predictors of TKRs were, at baseline, higher WOMAC pain and function scores, presence of bone marrow lesions (BMLs), and higher C-reactive protein levels. Loss of medial cartilage volume and increase in WOMAC pain and function at one-year were also predictors of TKR. Multivariate analyses revealed that baseline presence of BML and higher WOMAC pain score were independent predictors. Time to occurrence of the TKR also favored the CS group versus placebo (log-rank, P = 0.094). CONCLUSION: Symptoms such as knee pain and function, presence of BML, and cartilage volume loss predict the long-term occurrence of a "hard" outcome such as TKR.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".