Auditory display of knee-joint vibration signals
Bibliographic record
Abstract
Sounds generated due to rubbing of knee-joint surfaces may lead to a potential tool for noninvasive assessment of articular cartilage degeneration. In the work reported in the present paper, an attempt is made to perform computer-assisted auscultation of knee joints by auditory display (AD) of vibration signals (also known as vibroarthrographic or VAG signals) emitted during active movement of the leg. Two types of AD methods are considered: audification and sonification. In audification, the VAG signals are scaled in time and frequency using a time-frequency distribution to facilitate aural analysis. In sonification, the instantaneous mean frequency and envelope of the VAG signals are derived and used to synthesize sounds that are expected to facilitate more accurate diagnosis than the original signals by improving their aural quality. Auditory classification experiments were performed by two orthopedic surgeons with 37 VAG signals including 19 normal and 18 abnormal cases. Sensitivity values (correct detection of abnormality) of 31%, 44%, and 83%, and overall classification accuracies of 53%, 40%, and 57% were obtained with the direct playback, audification, and sonification methods, respectively. The corresponding d' scores were estimated to be 1.10. -0.36, and 0.55. The high sensitivity of the sonification method indicates that the technique could lead to improved detection of knee-joint abnormalities; however, additional work is required to improve its specificity and achieve better overall performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".