A Comparative Study to Measure Audible Release (Crepitus) From Human Joints
Bibliographic record
Abstract
Non-impulse and Impulse based therapy approaches are often preferred over surgery because they are non-invasive. These techniques are often accompanied by audible release (crepitus) from the human joints. The sounds emitted by a damaged joint can be correlated with an ailment, which can provide more information to a practitioner and improve the success of the treatment. However, background noise is usually mixed with the captured sound, which can affect the quality of diagnosis. Cumbersome setup is another problem in widespread use of audio-based treatment methods. The goal of this study is to design a compact clinical tool to excite and capture crepitus, while eliminating the background noise. In this regard, three different sensors — an accelerometer, a condenser microphone, and a contact microphone — are utilized to record crepitus, and they are compared with one another based on their noise content, ease of use, and cost efficiency. The results of this study showed that contact microphone is the most suitable sensor for this purpose. Based on this selection, a compact design is suggested for the clinical tool, which includes a contact microphone.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".