193. Novel Classification of Knee Osteoarthritis Severity Based on Spatiotemporal Analysis
Bibliographic record
Abstract
Background: Knee OA is a common disease with estimated prevalence of 30% in patients over the age of 60. Classification systems have focused on radiology and clinical symptoms alone. The gait changes in patients suffering from knee OA are well documented in the literature and include, among other, lower step length and velocity. In this work we are suggesting a new classification method for knee OA based on spatiotemporal gait analysis. Methods: Gait analysis of 2900 patients from AposTherapy dataset (AposTherapy, Herzeliya, Israel) suffering from knee OA were included in the study. Men and women were analysed separately. The analysis included three stages: clustering, classification and clinical validation. Clustering of gait analysis data by the k-means method created four groups. Two-thirds of the patients were used to create a simplified classification tree algorithm. The model’s accuracy was checked by using the remaining one-third of the patients. Clinical validation of the classification method was done by SF-36 and Western Ontario and McMaster Osteoarthritis Index (WOMAC) questionnaires.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".