A Conference Mechanism for the Simultaneous Transmission of Voice and Medical Information using SIP
Bibliographic record
Abstract
New services based on session initiation protocol (SIP) signaling, such as videoconferencing and presence-based applications, are emerging. New services mean new sets of requirements which, in turn, mean that existing architectures or protocols are not always adequate. A concrete example of such a new service is a system enabling an ambulance attendant to communicate with a number of physicians at the same time. In addition to regular voice transmission, the conference thus established would allow participants to study a patient's biometric data and share annotations directly. After formalizing the new set of requirements this service calls for, we turned to the most common mechanisms available and found that there are none perfectly suited to this new application. We therefore considered a less well-known mechanism, which is based on SIP and the implementation of a full mesh topology. This solution provides strong reliability, easy integration in future SIP user agents, powerful security capabilities and other essential features. ecgML, an innovative open XML-based format developed for the storage and transmission of electrocardiograms (ECGs), was analyzed and integrated into the proposed solution for the transmission of the patient's biometric data
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.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".