Electronic Stethoscope for eHealth and Telemedicine
Bibliographic record
Abstract
The success of ehealth and telemedicine depend on the development of advanced medical equipment that can streamline the tasks of medical data collection, processing and archiving. Audio signals detected by stethoscopes are some of the basic data used by medical doctors on a daily basis but there is no systematic approach to process, transmit and archive the collected data digitally. This may due to the fact that the electronic stethoscopes are still relatively expensive. General use of electronic stethoscope by physicians will not happen until the cost is dropped to an “affordable” level and/or the stethoscope has additional features and capabilities not found in current versions. Affordable electronic stethoscopes enable all doctors to collect and archive acoustic medical signal easily. Furthermore, a feature-laden device may also be used by non-medical specialists to collect data remotely for medical doctors. Also, a “user friendly” version of the electronic stethoscope that elderlies may easily use to transmit their own heart and lung audio signals to their family physicians via the telephone or Internet would be a good tool for telemedicine. We are investigating the desirable features and requirements, and formulating the specification for an affordable electronic stethoscope for ehealth and telemedicine. Issues of data collection, pre-processing, transmission, and storage, as well as future possible expansion to accommodate additional modules for further data post-processing and integration with other electronic medical devices, are considered.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".