HIERARCHICAL ANALYSIS AND CLASSIFICATION OF ASYMPTOMATIC AND KNEE OSTEOARTHRITIS GAIT PATTERNS USING A WAVELET REPRESENTATION OF KINETIC DATA AND THE NEAREST NEIGHBOR CLASSIFIER
Bibliographic record
Abstract
The purpose of this study is twofold: (1) to develop a classification method to distinguish between asymptomatic (AS) and knee osteoarthritis (OA) gait patterns using ground reaction force (GRF) measurements, and (2) to investigate OA severity within OA gait patterns. Features were first extracted from the GRF vectors to be used for classification. We investigated a two-level hierarchical classification and analysis method using the nearest neighbor rule. At the first level, the GRF data were classified into two classes: AS and OA. At the second level, the GRF data of OA patients were classified according to the pathology severity. The OA patients were grouped into two OA severity categories according to the Kellgren and Lawrence (KL) scale: KL 1 and KL 2 for one category, and KL 3 and KL 4 for the other. Experiments were conducted using data of 42 cases, 16 AS and 26 pathological. The method discriminated between AS and OA subjects with an accuracy of 38 of 42 cases, and assessed the severity correctly with an accuracy of 20 of 26 cases. These results demonstrated the validity of both, the feature and the classifier, for automatic classification of AS and knee OA gait patterns and for analysis of OA severity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".