OpenTera: A Framework for TelehealthApplications
Bibliographic record
Abstract
OpenTera is a microservice-based framework primarily developed to support telehealth research projects and real-world deployment.This project has 20 years of experience linking at-home participants to remote users (such as clinicians, researchers, healthcare, and professionals) with audio-video-data connections and in-the-field sensors, such as biometrics, wearable, and robotics devices.Applications of the OpenTera framework are not limited to research projects and could exist in clinical environments.Most telehealth-based research projects require a common data structure: data collection sites, projects, participants, and sessions, including various recorded data types from sensors or other sources.They also require standard features: user authentication based on various access roles, the ability to add new features based on specific project needs, ease of use for the participant, and secure data hosting.These features are also shared between research projects: videoconferencing with specific health-related features (e.g., angles measurement, timers), surveys data collection, data analysis, and exportation.Many available solutions are costly, feature-limited, proprietary (e.g., can hardly be adapted for research purposes, and raw data is more complex to access), or hard to deploy in telehealth.OpenTera was built for extensibility to provide research projects complete control over their data and hosting.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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".