Clinical Investigation of Knee-Joint Sounds in Patients With Patellofemoral Syndrome
Bibliographic record
Abstract
Patellofemoral syndrome is a common disorder of the knee-joint that is known to present difficulty in clinical assessment, diagnosis and treatment. Currently, clinicians use various conservative and non-conservative methods for treating the disorder, but there are no tools for providing good, quantitative measures of their effectiveness. The goal of this research is to look at the use of knee-joint sound as a clinical tool for progress assessment in rehabilitation of patellofemoral syndrome. There is a significant amount of literature on the use of acoustics in joint research, including the knee-joint. In the literature on knee-joint sound analysis, many researchers have attempted to use sound as a diagnostic tool. It is often problematic to try and identify a single, acoustic ‘signature’ that is characteristic of a specific disorder. In this research, relative changes in joint sounds will be used in analysis rather than absolute measurement. Changes of the knee-joint sounds throughout different treatment modalities will be compared to the patient base line in order to provide an indicator of treatment progress.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".