RETROSPECTIVE EVALUATION OF THE IMPACT OF KNEE OSTEOARTHRITIS SEVERITY ON TREATMENT
Bibliographic record
Abstract
Background: Knee osteoarthritis (OA) affects approximately 528 million people worldwide, representing a significant healthcare burden with variable treatment outcomes based on disease severity. Understanding the relationship between severity grading and treatment effectiveness is crucial for optimizing patient management strategies. Objective: To evaluate the impact of knee osteoarthritis severity, assessed using the Kellgren-Lawrence (KL) grading system, on treatment outcomes over a 24-month longitudinal period. Methods: This retrospective longitudinal study analyzed 1,247 patients with knee OA treated between January 2020 and December 2023. Patients were stratified by KL grades (0-4) and treatment modalities (conservative vs. surgical). Primary outcomes included pain scores using Visual Analog Scale (VAS), functional outcomes using Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and Knee injury and Osteoarthritis Outcome Score (KOOS), and quality of life measures at baseline, 6, 12, and 24 months. Results: Patients with KL grades 0-2 showed significantly better responses to conservative treatments, with mean VAS scores improving from 6.2±1.8 to 2.4±1.2 (p<0.001). Advanced OA (KL grades 3-4) demonstrated superior outcomes with surgical interventions, achieving 78% functional improvement compared to 34% with conservative management. Treatment failure rates increased with severity grade, ranging from 12% in KL grade 1 to 67% in KL grade 4 for conservative treatments. Conclusion: Knee osteoarthritis severity significantly influences treatment outcomes, with early-stage disease responding favorably to conservative management while advanced stages require surgical intervention for optimal results. These findings support severity-based treatment algorithms for improved patient outcomes. Keywords: Knee osteoarthritis, Kellgren-Lawrence grading, treatment outcomes, conservative management, surgical intervention, longitudinal study, functional assessment
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".