Towards a low-cost point-of-care screening platform for electronic auscultation of vital body sounds
Bibliographic record
Abstract
Analyzing pathological sounds is a simple test for understanding what is happening inside the body. This process of listening to the body is traditionally done using a Littmann stethoscope. Traditional stethoscopes are limited by inadequate sound amplification. Thus, if the sound of interest is of low amplitude, as is the case with biomedical sounds, detecting them is difficult. The electronic stethoscope (eStethoscope) platform is designed to facilitate doctors in the act of listening to body sounds. This is achieved by enhancing the functionality of traditional stethoscopes using electronic instrumentation. Additionally, using modern signal processing tools, the eStethoscope serves as a screening and monitoring aid to physicians and a learning aid for students. Preliminary results show that the signals acquired are distinguishable to have originated from their respective sources (heart, lung, and knee). The eStethoscope demonstrates the practicality of using low-cost instrumentation to obtain signal quality that is comparable to clinical grade signals, adding convenience to both the physician and the patient.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".