Electronic Stethoscope for eHealth and Telemedicine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it